model_cancer.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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      "cell_type": "code",
      "execution_count": 1,
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        "id": "tyI4o42aEcsI",
        "outputId": "e768cc1d-4611-4dfd-e74d-a485080b9bda"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ],
      "source": [
        "pip install tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# installation des packages\n",
        "from sklearn import datasets\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "LojCUOREEt1M"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#telecharger la dataset\n",
        "\n",
        "df=datasets.load_breast_cancer()"
      ],
      "metadata": {
        "id": "iPawa83DFeo2"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#decouvrir la base de données\n",
        "\n",
        "print(df.DESCR)"
      ],
      "metadata": {
        "id": "twJ5lI-7EuCx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#variable independates (resultat)\n",
        "df.target"
      ],
      "metadata": {
        "id": "5I96I95AEuFY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#les données (caracteristiques)\n",
        "df.data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bVMFZ2iEuH2",
        "outputId": "0f5ad311-06da-4407-ecad-db4e55aa7cda"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
              "        1.189e-01],\n",
              "       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
              "        8.902e-02],\n",
              "       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
              "        8.758e-02],\n",
              "       ...,\n",
              "       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
              "        7.820e-02],\n",
              "       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
              "        1.240e-01],\n",
              "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
              "        7.039e-02]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "On voit que les données ont des valeurs non standard on va donc les traités avec sklearn."
      ],
      "metadata": {
        "id": "g6VAGBr8Hq8l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#preparons les données\n",
        "\n",
        "X= df.data\n",
        "Y=df.target"
      ],
      "metadata": {
        "id": "s_sFtgVZEuKz"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#standarliser les donées (les données seront maintenant entre -1 et 1)\n",
        "\n",
        "scaler=StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "X_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jSrw7LDhHF3K",
        "outputId": "9fcdd228-53a2-41a3-da11-31230f3435b5"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.09706398, -2.07333501,  1.26993369, ...,  2.29607613,\n",
              "         2.75062224,  1.93701461],\n",
              "       [ 1.82982061, -0.35363241,  1.68595471, ...,  1.0870843 ,\n",
              "        -0.24388967,  0.28118999],\n",
              "       [ 1.57988811,  0.45618695,  1.56650313, ...,  1.95500035,\n",
              "         1.152255  ,  0.20139121],\n",
              "       ...,\n",
              "       [ 0.70228425,  2.0455738 ,  0.67267578, ...,  0.41406869,\n",
              "        -1.10454895, -0.31840916],\n",
              "       [ 1.83834103,  2.33645719,  1.98252415, ...,  2.28998549,\n",
              "         1.91908301,  2.21963528],\n",
              "       [-1.80840125,  1.22179204, -1.81438851, ..., -1.74506282,\n",
              "        -0.04813821, -0.75120669]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#onva séparer les donnéees en données d'entrainement et en données de test\n",
        "#on va utiliser 20% des donnéees pour les test donc 0.2\n",
        "X_train,X_test,Y_train,Y_test=train_test_split(X_scaled,Y,test_size=0.2,random_state=42)"
      ],
      "metadata": {
        "id": "Q6XoxUoAHF5-"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "Si vous ne spécifiez pas le random_statedans votre code, chaque fois que vous exécutez (exécutez) votre code, une nouvelle valeur aléatoire est générée et les ensembles de données d'entraînement et de test auraient des valeurs différentes à chaque fois.\n",
        "\n",
        "Cependant, si une valeur fixe est attribuée comme random_state = 42alors, quel que soit le nombre de fois que vous exécutez votre code, le résultat serait le même, c'est-à-dire les mêmes valeurs dans les ensembles de données d'entraînement et de test.\n"
      ],
      "metadata": {
        "id": "4G1qSgiCJ703"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#tailles de dataset train et test\n",
        "\n",
        "print(X_train.shape,X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_I3WKhnPHF8o",
        "outputId": "dc4838ec-d548-4f20-808c-acca2d7a8902"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(455, 30) (114, 30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#construction des réseau de neuronnes\n",
        "#initialisons notre model(creation du reseau de neuronne)\n",
        "\n",
        "model=tf.keras.models.Sequential()"
      ],
      "metadata": {
        "id": "guEFL04QKRQ-"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#nous allons données le nombre de noeud en entrées c'est à dire le nombre de variables en entrée avec units=nb et aussi l'algorithme d'activation qui est l'algorithme qui permet de determiner la sorties\n",
        "#les algorithmes d'activation les plus utilisées sont sigmoid, relu,  hyper Tangent et identify\n",
        "\n",
        "model.add(tf.keras.layers.Dense(units=15,activation='relu',kernel_initializer='uniform',input_dim=30))\n",
        "\n",
        "#model.add(tf.keras.layers.Dense(units=nb de noeud en entré,activation='algorithme d'activation à utiliser',kernel_initializer='initialisation des poids qui seront corriger au fur avec l'algorithme de cout',input_dim=nombre de variables total de la dataset)\n"
      ],
      "metadata": {
        "id": "kTlPdkXcKyzl"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#nous allon maintenant creer la couche de sortie avec l'algorithme sigmoid et non relu cette fois-ci\n",
        "model.add(tf.keras.layers.Dense(units=1,activation='sigmoid',kernel_initializer='uniform'))\n"
      ],
      "metadata": {
        "id": "ASRbBrtnKyb3"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#notre algorithme de cout\n",
        "#loss est la fonction de cout\n",
        "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['binary_accuracy'])\n",
        "\n",
        "#loss =binary..... parcequ'on a deux sortie donc binaire et metric qui calcul le nombre de fois qu'on se trompe"
      ],
      "metadata": {
        "id": "ESfgjLivHF_S"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#visualisons notre reseau de neuronne et voyons les différentes couches ainsi que le nomnre de noeud de chaque couche\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RfsYqb0wHGBx",
        "outputId": "9875ba7f-1dbd-4f43-dfe9-ea28f73cfde1"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_4 (Dense)             (None, 15)                465       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 16        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 481\n",
            "Trainable params: 481\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#entrainons notre reseau de neuronne(affectation des poids adequats)\n",
        "\n",
        "model.fit(X_train, Y_train,epochs=100)\n",
        "\n",
        "#epochs designe le nombre de fois qu'on entraine notre model en ajustant les poids"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tNlT2qX-HGEb",
        "outputId": "df15306d-fdfd-4f87-f38c-0125ac792dba"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "15/15 [==============================] - 1s 2ms/step - loss: 0.6801 - binary_accuracy: 0.8681\n",
            "Epoch 2/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.6440 - binary_accuracy: 0.9209\n",
            "Epoch 3/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.5859 - binary_accuracy: 0.9275\n",
            "Epoch 4/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.5084 - binary_accuracy: 0.9275\n",
            "Epoch 5/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.4273 - binary_accuracy: 0.9297\n",
            "Epoch 6/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.3520 - binary_accuracy: 0.9363\n",
            "Epoch 7/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2927 - binary_accuracy: 0.9407\n",
            "Epoch 8/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2471 - binary_accuracy: 0.9451\n",
            "Epoch 9/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.2151 - binary_accuracy: 0.9516\n",
            "Epoch 10/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1891 - binary_accuracy: 0.9538\n",
            "Epoch 11/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.1700 - binary_accuracy: 0.9582\n",
            "Epoch 12/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.1543 - binary_accuracy: 0.9626\n",
            "Epoch 13/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1419 - binary_accuracy: 0.9714\n",
            "Epoch 14/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1316 - binary_accuracy: 0.9736\n",
            "Epoch 15/100\n",
            "15/15 [==============================] - 0s 4ms/step - loss: 0.1226 - binary_accuracy: 0.9758\n",
            "Epoch 16/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1149 - binary_accuracy: 0.9758\n",
            "Epoch 17/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1086 - binary_accuracy: 0.9780\n",
            "Epoch 18/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.1032 - binary_accuracy: 0.9780\n",
            "Epoch 19/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0984 - binary_accuracy: 0.9780\n",
            "Epoch 20/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0944 - binary_accuracy: 0.9780\n",
            "Epoch 21/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0908 - binary_accuracy: 0.9780\n",
            "Epoch 22/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0877 - binary_accuracy: 0.9802\n",
            "Epoch 23/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0851 - binary_accuracy: 0.9824\n",
            "Epoch 24/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0825 - binary_accuracy: 0.9824\n",
            "Epoch 25/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0802 - binary_accuracy: 0.9846\n",
            "Epoch 26/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0783 - binary_accuracy: 0.9868\n",
            "Epoch 27/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0765 - binary_accuracy: 0.9868\n",
            "Epoch 28/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0748 - binary_accuracy: 0.9868\n",
            "Epoch 29/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0730 - binary_accuracy: 0.9868\n",
            "Epoch 30/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0715 - binary_accuracy: 0.9868\n",
            "Epoch 31/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0702 - binary_accuracy: 0.9868\n",
            "Epoch 32/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0688 - binary_accuracy: 0.9868\n",
            "Epoch 33/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0676 - binary_accuracy: 0.9868\n",
            "Epoch 34/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0665 - binary_accuracy: 0.9868\n",
            "Epoch 35/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0655 - binary_accuracy: 0.9868\n",
            "Epoch 36/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0644 - binary_accuracy: 0.9868\n",
            "Epoch 37/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0636 - binary_accuracy: 0.9868\n",
            "Epoch 38/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0627 - binary_accuracy: 0.9868\n",
            "Epoch 39/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0617 - binary_accuracy: 0.9846\n",
            "Epoch 40/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0609 - binary_accuracy: 0.9868\n",
            "Epoch 41/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0602 - binary_accuracy: 0.9846\n",
            "Epoch 42/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0595 - binary_accuracy: 0.9868\n",
            "Epoch 43/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0587 - binary_accuracy: 0.9868\n",
            "Epoch 44/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0581 - binary_accuracy: 0.9868\n",
            "Epoch 45/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0575 - binary_accuracy: 0.9846\n",
            "Epoch 46/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0569 - binary_accuracy: 0.9868\n",
            "Epoch 47/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0562 - binary_accuracy: 0.9846\n",
            "Epoch 48/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0558 - binary_accuracy: 0.9846\n",
            "Epoch 49/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0553 - binary_accuracy: 0.9846\n",
            "Epoch 50/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0547 - binary_accuracy: 0.9846\n",
            "Epoch 51/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0542 - binary_accuracy: 0.9846\n",
            "Epoch 52/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0539 - binary_accuracy: 0.9846\n",
            "Epoch 53/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0534 - binary_accuracy: 0.9846\n",
            "Epoch 54/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0529 - binary_accuracy: 0.9846\n",
            "Epoch 55/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0524 - binary_accuracy: 0.9846\n",
            "Epoch 56/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0520 - binary_accuracy: 0.9846\n",
            "Epoch 57/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0515 - binary_accuracy: 0.9846\n",
            "Epoch 58/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0510 - binary_accuracy: 0.9846\n",
            "Epoch 59/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0507 - binary_accuracy: 0.9846\n",
            "Epoch 60/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0503 - binary_accuracy: 0.9846\n",
            "Epoch 61/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0500 - binary_accuracy: 0.9868\n",
            "Epoch 62/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0494 - binary_accuracy: 0.9868\n",
            "Epoch 63/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0492 - binary_accuracy: 0.9846\n",
            "Epoch 64/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0487 - binary_accuracy: 0.9846\n",
            "Epoch 65/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0485 - binary_accuracy: 0.9868\n",
            "Epoch 66/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0478 - binary_accuracy: 0.9846\n",
            "Epoch 67/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0475 - binary_accuracy: 0.9868\n",
            "Epoch 68/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0472 - binary_accuracy: 0.9868\n",
            "Epoch 69/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0467 - binary_accuracy: 0.9868\n",
            "Epoch 70/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0464 - binary_accuracy: 0.9868\n",
            "Epoch 71/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0460 - binary_accuracy: 0.9868\n",
            "Epoch 72/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0457 - binary_accuracy: 0.9868\n",
            "Epoch 73/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0453 - binary_accuracy: 0.9868\n",
            "Epoch 74/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0451 - binary_accuracy: 0.9868\n",
            "Epoch 75/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0448 - binary_accuracy: 0.9868\n",
            "Epoch 76/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0444 - binary_accuracy: 0.9868\n",
            "Epoch 77/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0441 - binary_accuracy: 0.9868\n",
            "Epoch 78/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0439 - binary_accuracy: 0.9868\n",
            "Epoch 79/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0436 - binary_accuracy: 0.9868\n",
            "Epoch 80/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0433 - binary_accuracy: 0.9868\n",
            "Epoch 81/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0430 - binary_accuracy: 0.9868\n",
            "Epoch 82/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0428 - binary_accuracy: 0.9868\n",
            "Epoch 83/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0422 - binary_accuracy: 0.9868\n",
            "Epoch 84/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0420 - binary_accuracy: 0.9868\n",
            "Epoch 85/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0416 - binary_accuracy: 0.9868\n",
            "Epoch 86/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0414 - binary_accuracy: 0.9868\n",
            "Epoch 87/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0411 - binary_accuracy: 0.9868\n",
            "Epoch 88/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0408 - binary_accuracy: 0.9890\n",
            "Epoch 89/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0405 - binary_accuracy: 0.9890\n",
            "Epoch 90/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0402 - binary_accuracy: 0.9890\n",
            "Epoch 91/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0398 - binary_accuracy: 0.9890\n",
            "Epoch 92/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0396 - binary_accuracy: 0.9890\n",
            "Epoch 93/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0393 - binary_accuracy: 0.9912\n",
            "Epoch 94/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0391 - binary_accuracy: 0.9912\n",
            "Epoch 95/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0387 - binary_accuracy: 0.9912\n",
            "Epoch 96/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0384 - binary_accuracy: 0.9912\n",
            "Epoch 97/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0382 - binary_accuracy: 0.9912\n",
            "Epoch 98/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0379 - binary_accuracy: 0.9912\n",
            "Epoch 99/100\n",
            "15/15 [==============================] - 0s 2ms/step - loss: 0.0377 - binary_accuracy: 0.9912\n",
            "Epoch 100/100\n",
            "15/15 [==============================] - 0s 3ms/step - loss: 0.0374 - binary_accuracy: 0.9912\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9b29614af0>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#testons le model\n",
        "test_loss,test_accuracy=model.evaluate(X_test,Y_test)\n",
        "print(f'Test reuissi {test_accuracy} et Test echoué {test_loss}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SMinFW8GHGHB",
        "outputId": "08a7724c-8116-4f5a-cdbb-bdd106bf98ee"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0512 - binary_accuracy: 0.9912\n",
            "Test reuissi 0.9912280440330505 et Test echoué 0.05122005194425583\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notre modèle marche très bien avec une précision de 99.12%"
      ],
      "metadata": {
        "id": "H9ZtKQmCT2eN"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8cewNcR9HGJz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2slIrQ4fHGMq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xy8X9LrNHGPY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AfxBttrhHGSo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "NMWJWEkrHBbY"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gfAmafaiEuOS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}