Churn Model of Customers.ipynb

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      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LeakyReLU, PReLU, ELU\n",
        "from keras.layers import Dropout\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# read the files\n",
        "data = pd.read_csv('../Churn_Modelling.csv')"
      ],
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      "execution_count": 56,
      "outputs": []
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        "data.head(5)"
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              "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
              "0          1    15634602  Hargrave          619    France  Female   42   \n",
              "1          2    15647311      Hill          608     Spain  Female   41   \n",
              "2          3    15619304      Onio          502    France  Female   42   \n",
              "3          4    15701354      Boni          699    France  Female   39   \n",
              "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",
              "3         93826.63       0  \n",
              "4         79084.10       0  "
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              "      <td>Boni</td>\n",
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              "      <td>Mitchell</td>\n",
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              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
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      "source": [
        "# visualizing categorical variables\n",
        "\n",
        "fig, ax = plt.subplots(3, 2, figsize = (18, 15))\n",
        "\n",
        "sns.countplot('Geography', hue = 'Exited', data = data, ax = ax[0][0])\n",
        "sns.countplot('Gender', hue = 'Exited', data = data, ax = ax[0][1])\n",
        "sns.countplot('Tenure', hue = 'Exited', data = data, ax = ax[1][0])\n",
        "sns.countplot('NumOfProducts', hue = 'Exited', data = data, ax = ax[1][1])\n",
        "sns.countplot('HasCrCard', hue = 'Exited', data = data, ax = ax[2][0])\n",
        "sns.countplot('IsActiveMember', hue = 'Exited', data = data, ax = ax[2][1])\n",
        "\n",
        "plt.tight_layout()\n",
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            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### pre-processsing"
      ],
      "metadata": {
        "id": "PCCc4KkZAOco"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check for missing values\n",
        "data.isnull().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVPVwfVNUKYx",
        "outputId": "0a6ff84f-cf6e-4df2-8fb1-9a4bfc740147"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RowNumber          False\n",
              "CustomerId         False\n",
              "Surname            False\n",
              "CreditScore        False\n",
              "Geography          False\n",
              "Gender             False\n",
              "Age                False\n",
              "Tenure             False\n",
              "Balance            False\n",
              "NumOfProducts      False\n",
              "HasCrCard          False\n",
              "IsActiveMember     False\n",
              "EstimatedSalary    False\n",
              "Exited             False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dropping useless columns \n",
        "\n",
        "data.drop(columns = ['RowNumber', 'CustomerId', 'Surname'], axis = 1, inplace = True)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "eMmnCSQW87R5",
        "outputId": "976e9c22-d57d-4902-f3c4-7c04eca458fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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",
              "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
              "0          1               1        101348.88       1  \n",
              "1          0               1        112542.58       0  \n",
              "2          1               0        113931.57       1  \n",
              "3          0               0         93826.63       0  \n",
              "4          1               1         79084.10       0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-39303bfe-8393-4e4d-b0b4-4d59276a9aea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>619</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>608</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>502</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>699</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39303bfe-8393-4e4d-b0b4-4d59276a9aea')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "          const element = document.querySelector('#df-39303bfe-8393-4e4d-b0b4-4d59276a9aea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.Geography.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zs-vMmAe9Kuc",
        "outputId": "5f9e50e6-e9d3-4f4d-9dd5-8d82cf4bfa98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "France     5014\n",
              "Germany    2509\n",
              "Spain      2477\n",
              "Name: Geography, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding categorical variables\n",
        "\n",
        "data['Geography'] = data['Geography'].map({'France' : 0, 'Germany' : 1, 'Spain' : 2})\n",
        "data['Gender'] = data['Gender'].map({'Male' : 0, 'Female' : 1})"
      ],
      "metadata": {
        "id": "XlzXle1I9Os3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "zfgq0omvgOBI",
        "outputId": "344f18fa-2e2f-48ad-dfda-d0a68d516d74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CreditScore  Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
              "0          619          0       1   42       2       0.00              1   \n",
              "1          608          2       1   41       1   83807.86              1   \n",
              "2          502          0       1   42       8  159660.80              3   \n",
              "3          699          0       1   39       1       0.00              2   \n",
              "4          850          2       1   43       2  125510.82              1   \n",
              "\n",
              "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
              "0          1               1        101348.88       1  \n",
              "1          0               1        112542.58       0  \n",
              "2          1               0        113931.57       1  \n",
              "3          0               0         93826.63       0  \n",
              "4          1               1         79084.10       0  "
            ],
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              "      <th>CreditScore</th>\n",
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              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
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              "      <td>608</td>\n",
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              "      <td>699</td>\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c3545fcc-2c54-4919-b373-1f96ef764ba6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "x = data.drop('Exited', axis = 1)\n",
        "y = to_categorical(data.Exited)"
      ],
      "metadata": {
        "id": "i-_6YYuQ9U-f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# splitting data into training set and test set\n",
        "\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": "a7bM41sF9dAo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling data\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "metadata": {
        "id": "lQA54b1E9fgA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "FFNN"
      ],
      "metadata": {
        "id": "CASMjSQW9hSW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating Independent Vector\n",
        "X = data.iloc[:, 3:-1].values\n",
        "#Creating Dependent Vector\n",
        "y = data.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "a7m-JBlViUID"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import BatchNormalization\n",
        "\n",
        "# initializing ann\n",
        "ann = Sequential()\n",
        "\n",
        "# adding the input layer\n",
        "ann.add(Dense(10, input_shape = (10, )))\n",
        "\n",
        "# add the first hidden layer\n",
        "ann.add(Dense(10, kernel_initializer = 'normal', activation = 'relu',))\n",
        "\n",
        "#dropout & batch normalization\n",
        "ann.add(Dropout(rate = 0.1))\n",
        "ann.add(BatchNormalization())\n",
        "\n",
        "#output layer\n",
        "ann.add(tf.keras.layers.Dense(units= 2, activation='sigmoid'))\n",
        "\n",
        "ann.compile(optimizer= 'rmsprop', loss= 'binary_crossentropy', metrics= ['accuracy'])\n",
        "ann_history = ann.fit(x_train, y_train, batch_size= 32, epochs= 50, validation_split= 0.3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0JykYl9ZQCT3",
        "outputId": "2828a581-70a3-4137-ea2a-1be070b94479"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "175/175 [==============================] - 2s 6ms/step - loss: 0.5950 - accuracy: 0.6930 - val_loss: 0.5156 - val_accuracy: 0.7958\n",
            "Epoch 2/50\n",
            "175/175 [==============================] - 2s 11ms/step - loss: 0.4616 - accuracy: 0.8066 - val_loss: 0.4493 - val_accuracy: 0.7971\n",
            "Epoch 3/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.4215 - accuracy: 0.8223 - val_loss: 0.4179 - val_accuracy: 0.8192\n",
            "Epoch 4/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.4047 - accuracy: 0.8339 - val_loss: 0.4103 - val_accuracy: 0.8275\n",
            "Epoch 5/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.4024 - accuracy: 0.8413 - val_loss: 0.3997 - val_accuracy: 0.8333\n",
            "Epoch 6/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3937 - accuracy: 0.8377 - val_loss: 0.3928 - val_accuracy: 0.8367\n",
            "Epoch 7/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3916 - accuracy: 0.8357 - val_loss: 0.3804 - val_accuracy: 0.8400\n",
            "Epoch 8/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3852 - accuracy: 0.8421 - val_loss: 0.3799 - val_accuracy: 0.8404\n",
            "Epoch 9/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3805 - accuracy: 0.8421 - val_loss: 0.3711 - val_accuracy: 0.8379\n",
            "Epoch 10/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3736 - accuracy: 0.8407 - val_loss: 0.3698 - val_accuracy: 0.8438\n",
            "Epoch 11/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3731 - accuracy: 0.8455 - val_loss: 0.3723 - val_accuracy: 0.8467\n",
            "Epoch 12/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3726 - accuracy: 0.8448 - val_loss: 0.3713 - val_accuracy: 0.8504\n",
            "Epoch 13/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3746 - accuracy: 0.8464 - val_loss: 0.3697 - val_accuracy: 0.8446\n",
            "Epoch 14/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3669 - accuracy: 0.8507 - val_loss: 0.3700 - val_accuracy: 0.8454\n",
            "Epoch 15/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3726 - accuracy: 0.8402 - val_loss: 0.3683 - val_accuracy: 0.8438\n",
            "Epoch 16/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3698 - accuracy: 0.8468 - val_loss: 0.3636 - val_accuracy: 0.8479\n",
            "Epoch 17/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3686 - accuracy: 0.8464 - val_loss: 0.3624 - val_accuracy: 0.8496\n",
            "Epoch 18/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3727 - accuracy: 0.8418 - val_loss: 0.3654 - val_accuracy: 0.8504\n",
            "Epoch 19/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3643 - accuracy: 0.8504 - val_loss: 0.3614 - val_accuracy: 0.8521\n",
            "Epoch 20/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3654 - accuracy: 0.8473 - val_loss: 0.3616 - val_accuracy: 0.8479\n",
            "Epoch 21/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3699 - accuracy: 0.8441 - val_loss: 0.3603 - val_accuracy: 0.8537\n",
            "Epoch 22/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3660 - accuracy: 0.8498 - val_loss: 0.3640 - val_accuracy: 0.8487\n",
            "Epoch 23/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3677 - accuracy: 0.8482 - val_loss: 0.3695 - val_accuracy: 0.8425\n",
            "Epoch 24/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3629 - accuracy: 0.8482 - val_loss: 0.3581 - val_accuracy: 0.8500\n",
            "Epoch 25/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3628 - accuracy: 0.8480 - val_loss: 0.3606 - val_accuracy: 0.8483\n",
            "Epoch 26/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3702 - accuracy: 0.8446 - val_loss: 0.3628 - val_accuracy: 0.8479\n",
            "Epoch 27/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3723 - accuracy: 0.8470 - val_loss: 0.3586 - val_accuracy: 0.8492\n",
            "Epoch 28/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3632 - accuracy: 0.8491 - val_loss: 0.3590 - val_accuracy: 0.8500\n",
            "Epoch 29/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3662 - accuracy: 0.8464 - val_loss: 0.3590 - val_accuracy: 0.8496\n",
            "Epoch 30/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3672 - accuracy: 0.8466 - val_loss: 0.3593 - val_accuracy: 0.8504\n",
            "Epoch 31/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3677 - accuracy: 0.8468 - val_loss: 0.3560 - val_accuracy: 0.8512\n",
            "Epoch 32/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3675 - accuracy: 0.8480 - val_loss: 0.3590 - val_accuracy: 0.8512\n",
            "Epoch 33/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3623 - accuracy: 0.8507 - val_loss: 0.3605 - val_accuracy: 0.8525\n",
            "Epoch 34/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3702 - accuracy: 0.8438 - val_loss: 0.3582 - val_accuracy: 0.8504\n",
            "Epoch 35/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3665 - accuracy: 0.8455 - val_loss: 0.3601 - val_accuracy: 0.8500\n",
            "Epoch 36/50\n",
            "175/175 [==============================] - 1s 6ms/step - loss: 0.3687 - accuracy: 0.8493 - val_loss: 0.3565 - val_accuracy: 0.8533\n",
            "Epoch 37/50\n",
            "175/175 [==============================] - 1s 6ms/step - loss: 0.3621 - accuracy: 0.8482 - val_loss: 0.3569 - val_accuracy: 0.8512\n",
            "Epoch 38/50\n",
            "175/175 [==============================] - 1s 5ms/step - loss: 0.3625 - accuracy: 0.8536 - val_loss: 0.3575 - val_accuracy: 0.8496\n",
            "Epoch 39/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3638 - accuracy: 0.8495 - val_loss: 0.3584 - val_accuracy: 0.8479\n",
            "Epoch 40/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3700 - accuracy: 0.8429 - val_loss: 0.3558 - val_accuracy: 0.8525\n",
            "Epoch 41/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3671 - accuracy: 0.8448 - val_loss: 0.3578 - val_accuracy: 0.8487\n",
            "Epoch 42/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3632 - accuracy: 0.8530 - val_loss: 0.3574 - val_accuracy: 0.8517\n",
            "Epoch 43/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3687 - accuracy: 0.8480 - val_loss: 0.3590 - val_accuracy: 0.8496\n",
            "Epoch 44/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3679 - accuracy: 0.8520 - val_loss: 0.3591 - val_accuracy: 0.8475\n",
            "Epoch 45/50\n",
            "175/175 [==============================] - 0s 3ms/step - loss: 0.3616 - accuracy: 0.8534 - val_loss: 0.3575 - val_accuracy: 0.8525\n",
            "Epoch 46/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3597 - accuracy: 0.8520 - val_loss: 0.3599 - val_accuracy: 0.8442\n",
            "Epoch 47/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3677 - accuracy: 0.8496 - val_loss: 0.3580 - val_accuracy: 0.8529\n",
            "Epoch 48/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3634 - accuracy: 0.8516 - val_loss: 0.3565 - val_accuracy: 0.8521\n",
            "Epoch 49/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3651 - accuracy: 0.8491 - val_loss: 0.3559 - val_accuracy: 0.8529\n",
            "Epoch 50/50\n",
            "175/175 [==============================] - 1s 3ms/step - loss: 0.3675 - accuracy: 0.8518 - val_loss: 0.3556 - val_accuracy: 0.8517\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = ann.predict(x_test)"
      ],
      "metadata": {
        "id": "UMQuezCAkbkW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicting the Test set results\n",
        "y_pred = ann.predict(x_test)\n",
        "for i in range(0, y_pred.size):\n",
        "    if y_pred[i] > 0.5:\n",
        "        y_pred[i] = 1\n",
        "    else:\n",
        "        y_pred[i] = 0\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lOAX9W_5n-7g",
        "outputId": "dec81b0c-0354-43bf-b832-5e1e8f5848c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 1ms/step\n",
            "[[1542   53]\n",
            " [ 230  175]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, mean_squared_error, classification_report\n",
        "accuracy = round(accuracy_score(y_test, y_pred) * 100, 2)\n",
        "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
        "report = classification_report(y_test, y_pred)\n",
        "print(\"The Classification report is as follows::\\n\", report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9iIQd5LooEvD",
        "outputId": "dfc86cbf-da29-452b-9830-e1b828dfd13e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Classification report is as follows::\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.97      0.92      1595\n",
            "           1       0.77      0.43      0.55       405\n",
            "\n",
            "    accuracy                           0.86      2000\n",
            "   macro avg       0.82      0.70      0.73      2000\n",
            "weighted avg       0.85      0.86      0.84      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize = (10, 8))\n",
        "sns.heatmap(cm, annot = True, fmt = '.0f', linewidths = .5, square = True)\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.title('Accuracy: {0}'.format(round(accuracy, 2)))\n",
        "plt.ylabel('Actual labels')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "xZzf_gsjoKV5",
        "outputId": "afd15235-743a-421e-a378-bc1fdd6f0f6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (12, 6))\n",
        "\n",
        "train_loss = ann_history.history['loss']\n",
        "val_loss = ann_history.history['val_loss'] \n",
        "epoch = range(1, 51)\n",
        "sns.lineplot(epoch, train_loss, label = 'Training Loss')\n",
        "sns.lineplot(epoch, val_loss, label = 'Validation Loss')\n",
        "plt.title('Training and Validation Loss\\n')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "RiktDphYGeCO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "outputId": "7e40fb54-e966-4bac-b668-1e23c52ccfea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (12, 6))\n",
        "\n",
        "train_loss = ann_history.history['accuracy']\n",
        "val_loss = ann_history.history['val_accuracy'] \n",
        "epoch = range(1, 51)\n",
        "sns.lineplot(epoch, train_loss, label = 'Training accuracy')\n",
        "sns.lineplot(epoch, val_loss, label = 'Validation accuracy')\n",
        "plt.title('Training and Validation Accuracy\\n')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "dQWnGsfVHXK0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "outputId": "97368dfe-fd06-441e-8472-ceb6ce1631dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = ann.evaluate(x_test, y_test)[1]\n",
        "\n",
        "print(f'Accuracy of model is {acc}')"
      ],
      "metadata": {
        "id": "IPKN_EifHen1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1f190d9-a278-4870-8444-8e7991191a88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 2ms/step - loss: 0.3500 - accuracy: 0.8580\n",
            "Accuracy of model is 0.8579999804496765\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "rDgpyLHMJQaE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Builing the function\n",
        "def ann_classifier():\n",
        "    ann = tf.keras.models.Sequential()\n",
        "    ann.add(tf.keras.layers.Dense(10, input_shape = (10, )))\n",
        "    ann.add(Dense(10, kernel_initializer = 'normal', activation = 'relu',))\n",
        "    ann.add(Dense(8, kernel_initializer = 'normal', activation = 'relu',))\n",
        "    ann.add(Dense(6, kernel_initializer = 'he_uniform',activation='relu'))\n",
        "    ann.add(Dropout(rate = 0.1))\n",
        "    ann.add(BatchNormalization())\n",
        "    ann.add(Dense(4, kernel_initializer = 'he_uniform',activation='relu'))\n",
        "    ann.add(Dense(2, kernel_initializer = 'he_uniform',activation='relu')) \n",
        "    ann.add(Dense(2, kernel_initializer = 'normal', activation = 'sigmoid'))\n",
        "    ann.compile(optimizer= 'adam', loss= 'binary_crossentropy', metrics= ['accuracy'])\n",
        "    return ann"
      ],
      "metadata": {
        "id": "cTD8UyfhJSYF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "# Passing values to KerasClassifier \n",
        "model = KerasClassifier(build_fn = ann_classifier, batch_size = 32, epochs = 50)\n",
        "# We are using 5 fold cross validation here\n",
        "accuracies = cross_val_score(estimator = model, X = x_train, y = y_train, cv = 5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cHXJKpSEJVGG",
        "outputId": "5c383d8a-d440-4bed-8556-b7e00846620d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: 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",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "200/200 [==============================] - 1s 3ms/step - loss: 0.5573 - accuracy: 0.7841\n",
            "Epoch 2/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.4461 - accuracy: 0.7983\n",
            "Epoch 3/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.4125 - accuracy: 0.8094\n",
            "Epoch 4/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3859 - accuracy: 0.8316\n",
            "Epoch 5/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3748 - accuracy: 0.8402\n",
            "Epoch 6/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3690 - accuracy: 0.8447\n",
            "Epoch 7/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3648 - accuracy: 0.8481\n",
            "Epoch 8/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3656 - accuracy: 0.8483\n",
            "Epoch 9/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3607 - accuracy: 0.8487\n",
            "Epoch 10/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3597 - accuracy: 0.8520\n",
            "Epoch 11/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3575 - accuracy: 0.8519\n",
            "Epoch 12/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3574 - accuracy: 0.8530\n",
            "Epoch 13/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3550 - accuracy: 0.8569\n",
            "Epoch 14/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3514 - accuracy: 0.8566\n",
            "Epoch 15/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3539 - accuracy: 0.8548\n",
            "Epoch 16/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3518 - accuracy: 0.8545\n",
            "Epoch 17/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3510 - accuracy: 0.8586\n",
            "Epoch 18/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3500 - accuracy: 0.8583\n",
            "Epoch 19/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3475 - accuracy: 0.8609\n",
            "Epoch 20/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3462 - accuracy: 0.8612\n",
            "Epoch 21/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3510 - accuracy: 0.8592\n",
            "Epoch 22/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3478 - accuracy: 0.8603\n",
            "Epoch 23/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3446 - accuracy: 0.8637\n",
            "Epoch 24/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3502 - accuracy: 0.8595\n",
            "Epoch 25/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3488 - accuracy: 0.8606\n",
            "Epoch 26/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3440 - accuracy: 0.8619\n",
            "Epoch 27/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3464 - accuracy: 0.8619\n",
            "Epoch 28/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3432 - accuracy: 0.8625\n",
            "Epoch 29/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3442 - accuracy: 0.8619\n",
            "Epoch 30/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3426 - accuracy: 0.8630\n",
            "Epoch 31/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3445 - accuracy: 0.8616\n",
            "Epoch 32/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3473 - accuracy: 0.8627\n",
            "Epoch 33/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3450 - accuracy: 0.8630\n",
            "Epoch 34/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3434 - accuracy: 0.8608\n",
            "Epoch 35/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3465 - accuracy: 0.8598\n",
            "Epoch 36/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3440 - accuracy: 0.8602\n",
            "Epoch 37/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3462 - accuracy: 0.8608\n",
            "Epoch 38/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3474 - accuracy: 0.8595\n",
            "Epoch 39/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3428 - accuracy: 0.8594\n",
            "Epoch 40/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3431 - accuracy: 0.8602\n",
            "Epoch 41/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3442 - accuracy: 0.8620\n",
            "Epoch 42/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3424 - accuracy: 0.8619\n",
            "Epoch 43/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3381 - accuracy: 0.8669\n",
            "Epoch 44/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3421 - accuracy: 0.8592\n",
            "Epoch 45/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3383 - accuracy: 0.8641\n",
            "Epoch 46/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3396 - accuracy: 0.8620\n",
            "Epoch 47/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3366 - accuracy: 0.8639\n",
            "Epoch 48/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3392 - accuracy: 0.8633\n",
            "Epoch 49/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3405 - accuracy: 0.8614\n",
            "Epoch 50/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3386 - accuracy: 0.8641\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3501 - accuracy: 0.8581\n",
            "Epoch 1/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.5368 - accuracy: 0.7869\n",
            "Epoch 2/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.4452 - accuracy: 0.7972\n",
            "Epoch 3/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.4229 - accuracy: 0.8055\n",
            "Epoch 4/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.4079 - accuracy: 0.8164\n",
            "Epoch 5/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3978 - accuracy: 0.8147\n",
            "Epoch 6/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3925 - accuracy: 0.8220\n",
            "Epoch 7/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3876 - accuracy: 0.8280\n",
            "Epoch 8/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3887 - accuracy: 0.8267\n",
            "Epoch 9/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3840 - accuracy: 0.8280\n",
            "Epoch 10/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3838 - accuracy: 0.8313\n",
            "Epoch 11/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3786 - accuracy: 0.8348\n",
            "Epoch 12/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3765 - accuracy: 0.8409\n",
            "Epoch 13/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3708 - accuracy: 0.8433\n",
            "Epoch 14/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3659 - accuracy: 0.8483\n",
            "Epoch 15/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3636 - accuracy: 0.8495\n",
            "Epoch 16/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3652 - accuracy: 0.8486\n",
            "Epoch 17/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3612 - accuracy: 0.8525\n",
            "Epoch 18/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3589 - accuracy: 0.8542\n",
            "Epoch 19/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3588 - accuracy: 0.8495\n",
            "Epoch 20/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3593 - accuracy: 0.8511\n",
            "Epoch 21/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3585 - accuracy: 0.8522\n",
            "Epoch 22/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3549 - accuracy: 0.8531\n",
            "Epoch 23/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3545 - accuracy: 0.8552\n",
            "Epoch 24/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3505 - accuracy: 0.8561\n",
            "Epoch 25/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3544 - accuracy: 0.8561\n",
            "Epoch 26/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3518 - accuracy: 0.8591\n",
            "Epoch 27/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3532 - accuracy: 0.8544\n",
            "Epoch 28/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3464 - accuracy: 0.8598\n",
            "Epoch 29/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3498 - accuracy: 0.8589\n",
            "Epoch 30/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3515 - accuracy: 0.8583\n",
            "Epoch 31/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3472 - accuracy: 0.8592\n",
            "Epoch 32/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3455 - accuracy: 0.8594\n",
            "Epoch 33/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3507 - accuracy: 0.8587\n",
            "Epoch 34/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3470 - accuracy: 0.8570\n",
            "Epoch 35/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3476 - accuracy: 0.8591\n",
            "Epoch 36/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3444 - accuracy: 0.8581\n",
            "Epoch 37/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3459 - accuracy: 0.8573\n",
            "Epoch 38/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3475 - accuracy: 0.8561\n",
            "Epoch 39/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3410 - accuracy: 0.8619\n",
            "Epoch 40/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3481 - accuracy: 0.8587\n",
            "Epoch 41/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3476 - accuracy: 0.8591\n",
            "Epoch 42/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3443 - accuracy: 0.8623\n",
            "Epoch 43/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3382 - accuracy: 0.8605\n",
            "Epoch 44/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3422 - accuracy: 0.8612\n",
            "Epoch 45/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3421 - accuracy: 0.8633\n",
            "Epoch 46/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3408 - accuracy: 0.8634\n",
            "Epoch 47/50\n",
            "200/200 [==============================] - 1s 6ms/step - loss: 0.3414 - accuracy: 0.8627\n",
            "Epoch 48/50\n",
            "200/200 [==============================] - 1s 4ms/step - loss: 0.3414 - accuracy: 0.8600\n",
            "Epoch 49/50\n",
            "200/200 [==============================] - 1s 6ms/step - loss: 0.3387 - accuracy: 0.8637\n",
            "Epoch 50/50\n",
            "200/200 [==============================] - 1s 6ms/step - loss: 0.3411 - accuracy: 0.8611\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3556 - accuracy: 0.8562\n",
            "Epoch 1/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.5291 - accuracy: 0.7873\n",
            "Epoch 2/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.4306 - accuracy: 0.7917\n",
            "Epoch 3/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.4108 - accuracy: 0.8014\n",
            "Epoch 4/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.4054 - accuracy: 0.8120\n",
            "Epoch 5/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.4012 - accuracy: 0.8158\n",
            "Epoch 6/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3950 - accuracy: 0.8230\n",
            "Epoch 7/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3935 - accuracy: 0.8245\n",
            "Epoch 8/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3866 - accuracy: 0.8306\n",
            "Epoch 9/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3791 - accuracy: 0.8372\n",
            "Epoch 10/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3768 - accuracy: 0.8405\n",
            "Epoch 11/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3711 - accuracy: 0.8464\n",
            "Epoch 12/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3767 - accuracy: 0.8456\n",
            "Epoch 13/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3689 - accuracy: 0.8461\n",
            "Epoch 14/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3704 - accuracy: 0.8447\n",
            "Epoch 15/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3670 - accuracy: 0.8461\n",
            "Epoch 16/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3695 - accuracy: 0.8441\n",
            "Epoch 17/50\n",
            "200/200 [==============================] - 1s 4ms/step - loss: 0.3665 - accuracy: 0.8483\n",
            "Epoch 18/50\n",
            "200/200 [==============================] - 1s 4ms/step - loss: 0.3657 - accuracy: 0.8500\n",
            "Epoch 19/50\n",
            "200/200 [==============================] - 1s 4ms/step - loss: 0.3650 - accuracy: 0.8450\n",
            "Epoch 20/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3631 - accuracy: 0.8461\n",
            "Epoch 21/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3651 - accuracy: 0.8453\n",
            "Epoch 22/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3630 - accuracy: 0.8495\n",
            "Epoch 23/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3629 - accuracy: 0.8492\n",
            "Epoch 24/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3625 - accuracy: 0.8480\n",
            "Epoch 25/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3618 - accuracy: 0.8458\n",
            "Epoch 26/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3581 - accuracy: 0.8550\n",
            "Epoch 27/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3601 - accuracy: 0.8509\n",
            "Epoch 28/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3597 - accuracy: 0.8544\n",
            "Epoch 29/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3550 - accuracy: 0.8533\n",
            "Epoch 30/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3561 - accuracy: 0.8552\n",
            "Epoch 31/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3545 - accuracy: 0.8548\n",
            "Epoch 32/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3556 - accuracy: 0.8558\n",
            "Epoch 33/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3505 - accuracy: 0.8592\n",
            "Epoch 34/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3523 - accuracy: 0.8569\n",
            "Epoch 35/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3512 - accuracy: 0.8586\n",
            "Epoch 36/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3512 - accuracy: 0.8566\n",
            "Epoch 37/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3498 - accuracy: 0.8573\n",
            "Epoch 38/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3437 - accuracy: 0.8617\n",
            "Epoch 39/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3491 - accuracy: 0.8589\n",
            "Epoch 40/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3501 - accuracy: 0.8572\n",
            "Epoch 41/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3486 - accuracy: 0.8598\n",
            "Epoch 42/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3444 - accuracy: 0.8602\n",
            "Epoch 43/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3447 - accuracy: 0.8609\n",
            "Epoch 44/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3440 - accuracy: 0.8639\n",
            "Epoch 45/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3425 - accuracy: 0.8606\n",
            "Epoch 46/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3436 - accuracy: 0.8639\n",
            "Epoch 47/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3474 - accuracy: 0.8595\n",
            "Epoch 48/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3451 - accuracy: 0.8594\n",
            "Epoch 49/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3445 - accuracy: 0.8619\n",
            "Epoch 50/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3437 - accuracy: 0.8616\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3581 - accuracy: 0.8519\n",
            "Epoch 1/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.5553 - accuracy: 0.7856\n",
            "Epoch 2/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.4565 - accuracy: 0.7973\n",
            "Epoch 3/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.4377 - accuracy: 0.7973\n",
            "Epoch 4/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.4200 - accuracy: 0.7973\n",
            "Epoch 5/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.4013 - accuracy: 0.7973\n",
            "Epoch 6/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3907 - accuracy: 0.8163\n",
            "Epoch 7/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3842 - accuracy: 0.8469\n",
            "Epoch 8/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3794 - accuracy: 0.8491\n",
            "Epoch 9/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3766 - accuracy: 0.8503\n",
            "Epoch 10/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3755 - accuracy: 0.8528\n",
            "Epoch 11/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3749 - accuracy: 0.8523\n",
            "Epoch 12/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3705 - accuracy: 0.8548\n",
            "Epoch 13/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3678 - accuracy: 0.8508\n",
            "Epoch 14/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3656 - accuracy: 0.8553\n",
            "Epoch 15/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3638 - accuracy: 0.8566\n",
            "Epoch 16/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3621 - accuracy: 0.8539\n",
            "Epoch 17/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3612 - accuracy: 0.8577\n",
            "Epoch 18/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3603 - accuracy: 0.8586\n",
            "Epoch 19/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3586 - accuracy: 0.8602\n",
            "Epoch 20/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3601 - accuracy: 0.8569\n",
            "Epoch 21/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3578 - accuracy: 0.8587\n",
            "Epoch 22/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3565 - accuracy: 0.8556\n",
            "Epoch 23/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3588 - accuracy: 0.8547\n",
            "Epoch 24/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3553 - accuracy: 0.8569\n",
            "Epoch 25/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3528 - accuracy: 0.8583\n",
            "Epoch 26/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3528 - accuracy: 0.8587\n",
            "Epoch 27/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3562 - accuracy: 0.8570\n",
            "Epoch 28/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3505 - accuracy: 0.8620\n",
            "Epoch 29/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3515 - accuracy: 0.8611\n",
            "Epoch 30/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3507 - accuracy: 0.8597\n",
            "Epoch 31/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3485 - accuracy: 0.8628\n",
            "Epoch 32/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3444 - accuracy: 0.8617\n",
            "Epoch 33/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3486 - accuracy: 0.8603\n",
            "Epoch 34/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3484 - accuracy: 0.8609\n",
            "Epoch 35/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3460 - accuracy: 0.8611\n",
            "Epoch 36/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3474 - accuracy: 0.8631\n",
            "Epoch 37/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3451 - accuracy: 0.8625\n",
            "Epoch 38/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3464 - accuracy: 0.8627\n",
            "Epoch 39/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3443 - accuracy: 0.8617\n",
            "Epoch 40/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3432 - accuracy: 0.8633\n",
            "Epoch 41/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3420 - accuracy: 0.8645\n",
            "Epoch 42/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3460 - accuracy: 0.8636\n",
            "Epoch 43/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3451 - accuracy: 0.8647\n",
            "Epoch 44/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3438 - accuracy: 0.8641\n",
            "Epoch 45/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3426 - accuracy: 0.8591\n",
            "Epoch 46/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3408 - accuracy: 0.8642\n",
            "Epoch 47/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3413 - accuracy: 0.8644\n",
            "Epoch 48/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3386 - accuracy: 0.8655\n",
            "Epoch 49/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3404 - accuracy: 0.8628\n",
            "Epoch 50/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3401 - accuracy: 0.8647\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3624 - accuracy: 0.8494\n",
            "Epoch 1/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.5289 - accuracy: 0.7955\n",
            "Epoch 2/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.4242 - accuracy: 0.7958\n",
            "Epoch 3/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.4090 - accuracy: 0.7958\n",
            "Epoch 4/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.4033 - accuracy: 0.7958\n",
            "Epoch 5/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3976 - accuracy: 0.8006\n",
            "Epoch 6/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3916 - accuracy: 0.8217\n",
            "Epoch 7/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3896 - accuracy: 0.8305\n",
            "Epoch 8/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3880 - accuracy: 0.8338\n",
            "Epoch 9/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3852 - accuracy: 0.8397\n",
            "Epoch 10/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3821 - accuracy: 0.8444\n",
            "Epoch 11/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3802 - accuracy: 0.8484\n",
            "Epoch 12/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3796 - accuracy: 0.8433\n",
            "Epoch 13/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3782 - accuracy: 0.8453\n",
            "Epoch 14/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3718 - accuracy: 0.8508\n",
            "Epoch 15/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3735 - accuracy: 0.8495\n",
            "Epoch 16/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3758 - accuracy: 0.8462\n",
            "Epoch 17/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3693 - accuracy: 0.8512\n",
            "Epoch 18/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3696 - accuracy: 0.8487\n",
            "Epoch 19/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3685 - accuracy: 0.8508\n",
            "Epoch 20/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3638 - accuracy: 0.8544\n",
            "Epoch 21/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3664 - accuracy: 0.8502\n",
            "Epoch 22/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3626 - accuracy: 0.8572\n",
            "Epoch 23/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3650 - accuracy: 0.8542\n",
            "Epoch 24/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3648 - accuracy: 0.8508\n",
            "Epoch 25/50\n",
            "200/200 [==============================] - 0s 2ms/step - loss: 0.3627 - accuracy: 0.8522\n",
            "Epoch 26/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3642 - accuracy: 0.8566\n",
            "Epoch 27/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3623 - accuracy: 0.8550\n",
            "Epoch 28/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3604 - accuracy: 0.8530\n",
            "Epoch 29/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3623 - accuracy: 0.8550\n",
            "Epoch 30/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3604 - accuracy: 0.8556\n",
            "Epoch 31/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3602 - accuracy: 0.8536\n",
            "Epoch 32/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3585 - accuracy: 0.8572\n",
            "Epoch 33/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3582 - accuracy: 0.8553\n",
            "Epoch 34/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3570 - accuracy: 0.8527\n",
            "Epoch 35/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3569 - accuracy: 0.8566\n",
            "Epoch 36/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3563 - accuracy: 0.8572\n",
            "Epoch 37/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3551 - accuracy: 0.8558\n",
            "Epoch 38/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3542 - accuracy: 0.8567\n",
            "Epoch 39/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3521 - accuracy: 0.8580\n",
            "Epoch 40/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3509 - accuracy: 0.8562\n",
            "Epoch 41/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3549 - accuracy: 0.8561\n",
            "Epoch 42/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3496 - accuracy: 0.8577\n",
            "Epoch 43/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3478 - accuracy: 0.8606\n",
            "Epoch 44/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3483 - accuracy: 0.8594\n",
            "Epoch 45/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3486 - accuracy: 0.8594\n",
            "Epoch 46/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3477 - accuracy: 0.8603\n",
            "Epoch 47/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3487 - accuracy: 0.8600\n",
            "Epoch 48/50\n",
            "200/200 [==============================] - 1s 2ms/step - loss: 0.3451 - accuracy: 0.8605\n",
            "Epoch 49/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3497 - accuracy: 0.8581\n",
            "Epoch 50/50\n",
            "200/200 [==============================] - 1s 3ms/step - loss: 0.3461 - accuracy: 0.8622\n",
            "50/50 [==============================] - 0s 2ms/step - loss: 0.3638 - accuracy: 0.8519\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the mean and standard deviation of the accuracies obtained\n",
        "mean = accuracies.mean()\n",
        "std_deviation = accuracies.std()\n",
        "print(\"Accuracy: {:.2f} %\".format(mean*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8UamT6E6Jll-",
        "outputId": "d8f97b6c-2f79-4352-af58-eefce9334239"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 85.35 %\n",
            "Standard Deviation: 0.32 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from keras.layers import BatchNormalization\n",
        "\n",
        "# Builing the function\n",
        "\n",
        "def ann_classifier( optimizer='adam'):\n",
        "    ann = tf.keras.models.Sequential()\n",
        "    ann.add(tf.keras.layers.Dense(10, input_shape = (10, )))\n",
        "    ann.add(Dense(10, kernel_initializer = 'normal', activation = 'relu',))\n",
        "    ann.add(Dense(8, kernel_initializer = 'normal', activation = 'relu',))\n",
        "    ann.add(Dense(6, kernel_initializer = 'normal',activation='relu'))\n",
        "    ann.add(Dropout(rate = 0.1))\n",
        "    ann.add(BatchNormalization())\n",
        "    ann.add(Dense(4, kernel_initializer = 'normal',activation='relu'))\n",
        "    ann.add(Dense(2, kernel_initializer = 'normal',activation='relu')) \n",
        "    ann.add(Dense(2, kernel_initializer = 'normal', activation = 'sigmoid'))\n",
        "    ann.compile(optimizer= 'adam', loss= 'binary_crossentropy', metrics= ['accuracy'])\n",
        "    return ann"
      ],
      "metadata": {
        "id": "Mb41yQffK09J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Passing values to KerasClassifier \n",
        "ann = KerasClassifier(build_fn = ann_classifier, batch_size = 32, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3yWW9tgdLoSU",
        "outputId": "164c75a5-825b-4f60-9e0a-32f1e9a7dd6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Using Grid Search CV to getting the best parameters\n",
        "parameters = {'batch_size': [25, 32],\n",
        "        'epochs': [50, 100],\n",
        "        'optimizer': ['adam', 'rmsprop']}\n",
        "\n",
        "grid_search = GridSearchCV(estimator = ann, param_grid = parameters, scoring = 'accuracy', cv = 5, n_jobs = -1)\n",
        "\n",
        "grid_search.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YrNIW16FLqn1",
        "outputId": "7ed7436e-0980-4772-a4c3-14a536677e0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan nan nan nan]\n",
            "  category=UserWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "320/320 [==============================] - 2s 2ms/step - loss: 0.4922 - accuracy: 0.7959\n",
            "Epoch 2/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.4367 - accuracy: 0.7960\n",
            "Epoch 3/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.4179 - accuracy: 0.7962\n",
            "Epoch 4/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.4010 - accuracy: 0.8284\n",
            "Epoch 5/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3985 - accuracy: 0.8341\n",
            "Epoch 6/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3902 - accuracy: 0.8409\n",
            "Epoch 7/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3822 - accuracy: 0.8431\n",
            "Epoch 8/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3773 - accuracy: 0.8481\n",
            "Epoch 9/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3738 - accuracy: 0.8496\n",
            "Epoch 10/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3667 - accuracy: 0.8535\n",
            "Epoch 11/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3666 - accuracy: 0.8534\n",
            "Epoch 12/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3622 - accuracy: 0.8543\n",
            "Epoch 13/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3599 - accuracy: 0.8560\n",
            "Epoch 14/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3619 - accuracy: 0.8524\n",
            "Epoch 15/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3575 - accuracy: 0.8540\n",
            "Epoch 16/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3580 - accuracy: 0.8535\n",
            "Epoch 17/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3540 - accuracy: 0.8571\n",
            "Epoch 18/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3560 - accuracy: 0.8561\n",
            "Epoch 19/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3570 - accuracy: 0.8541\n",
            "Epoch 20/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3603 - accuracy: 0.8522\n",
            "Epoch 21/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3536 - accuracy: 0.8564\n",
            "Epoch 22/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3539 - accuracy: 0.8545\n",
            "Epoch 23/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3483 - accuracy: 0.8591\n",
            "Epoch 24/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3506 - accuracy: 0.8560\n",
            "Epoch 25/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3510 - accuracy: 0.8569\n",
            "Epoch 26/50\n",
            "320/320 [==============================] - 1s 3ms/step - loss: 0.3524 - accuracy: 0.8539\n",
            "Epoch 27/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3485 - accuracy: 0.8597\n",
            "Epoch 28/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3526 - accuracy: 0.8579\n",
            "Epoch 29/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3515 - accuracy: 0.8579\n",
            "Epoch 30/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3509 - accuracy: 0.8564\n",
            "Epoch 31/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3505 - accuracy: 0.8590\n",
            "Epoch 32/50\n",
            "320/320 [==============================] - 1s 3ms/step - loss: 0.3479 - accuracy: 0.8558\n",
            "Epoch 33/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3487 - accuracy: 0.8544\n",
            "Epoch 34/50\n",
            "320/320 [==============================] - 1s 3ms/step - loss: 0.3477 - accuracy: 0.8572\n",
            "Epoch 35/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3514 - accuracy: 0.8585\n",
            "Epoch 36/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3478 - accuracy: 0.8580\n",
            "Epoch 37/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3506 - accuracy: 0.8597\n",
            "Epoch 38/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3492 - accuracy: 0.8589\n",
            "Epoch 39/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3501 - accuracy: 0.8601\n",
            "Epoch 40/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3472 - accuracy: 0.8616\n",
            "Epoch 41/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3458 - accuracy: 0.8597\n",
            "Epoch 42/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3467 - accuracy: 0.8593\n",
            "Epoch 43/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3459 - accuracy: 0.8583\n",
            "Epoch 44/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3487 - accuracy: 0.8579\n",
            "Epoch 45/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3426 - accuracy: 0.8626\n",
            "Epoch 46/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3449 - accuracy: 0.8595\n",
            "Epoch 47/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3455 - accuracy: 0.8572\n",
            "Epoch 48/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3450 - accuracy: 0.8597\n",
            "Epoch 49/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3439 - accuracy: 0.8605\n",
            "Epoch 50/50\n",
            "320/320 [==============================] - 1s 2ms/step - loss: 0.3463 - accuracy: 0.8596\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5,\n",
              "             estimator=<keras.wrappers.scikit_learn.KerasClassifier object at 0x7f9ddd90e450>,\n",
              "             n_jobs=-1,\n",
              "             param_grid={'batch_size': [25, 32], 'epochs': [50, 100],\n",
              "                         'optimizer': ['adam', 'rmsprop']},\n",
              "             scoring='accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VyRkU8vuNi7v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_accuracy = grid_search.best_score_\n",
        "best_parameters = grid_search.best_params_"
      ],
      "metadata": {
        "id": "kBPWwwljMHj9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Best Accuracy: {:.2f} %\".format(best_accuracy*100))\n",
        "print(\"Best Parameters:\", best_parameters)"
      ],
      "metadata": {
        "id": "cU0yk7ZeXfz0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0e4ca75-5242-4a77-c9f8-a1a036427008"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Accuracy: nan %\n",
            "Best Parameters: {'batch_size': 25, 'epochs': 50, 'optimizer': 'adam'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# defining the layers\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "model_tunned = Sequential()\n",
        "model_tunned.add(Dense(10, input_shape = (10, )))\n",
        "model_tunned.add(Dense(10, kernel_initializer = 'normal', activation = 'relu',))\n",
        "model_tunned.add(Dense(8, kernel_initializer = 'normal', activation = 'relu'))\n",
        "model_tunned.add(Dense(6, kernel_initializer = 'normal',activation='relu'))\n",
        "model_tunned.add(Dropout(rate = 0.1))\n",
        "model_tunned.add(BatchNormalization())\n",
        "model_tunned.add(Dense(4, kernel_initializer = 'normal',activation='relu'))\n",
        "model_tunned.add(Dense(2, kernel_initializer = 'normal',activation='relu'))\n",
        "model_tunned.add(Dense(1, kernel_initializer = 'normal', activation = 'sigmoid'))\n",
        "model_tunned.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
        "anntunned_history =model_tunned.fit(x_train, y_train, batch_size= 25, epochs= 50, validation_split= 0.3)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UqCnaLbmNj9K",
        "outputId": "94faadfa-0a9a-4829-a060-b78024dbf7eb"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "224/224 [==============================] - 3s 5ms/step - loss: 0.5186 - accuracy: 0.7948 - val_loss: 0.4999 - val_accuracy: 0.7958\n",
            "Epoch 2/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.4231 - accuracy: 0.7961 - val_loss: 0.4381 - val_accuracy: 0.7958\n",
            "Epoch 3/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.4076 - accuracy: 0.7961 - val_loss: 0.3977 - val_accuracy: 0.7958\n",
            "Epoch 4/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3957 - accuracy: 0.8037 - val_loss: 0.3957 - val_accuracy: 0.8454\n",
            "Epoch 5/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3906 - accuracy: 0.8427 - val_loss: 0.3962 - val_accuracy: 0.8375\n",
            "Epoch 6/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3851 - accuracy: 0.8395 - val_loss: 0.3874 - val_accuracy: 0.8421\n",
            "Epoch 7/50\n",
            "224/224 [==============================] - 1s 3ms/step - loss: 0.3795 - accuracy: 0.8487 - val_loss: 0.3808 - val_accuracy: 0.8483\n",
            "Epoch 8/50\n",
            "224/224 [==============================] - 1s 3ms/step - loss: 0.3787 - accuracy: 0.8479 - val_loss: 0.3806 - val_accuracy: 0.8500\n",
            "Epoch 9/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3768 - accuracy: 0.8532 - val_loss: 0.3762 - val_accuracy: 0.8521\n",
            "Epoch 10/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3702 - accuracy: 0.8536 - val_loss: 0.3810 - val_accuracy: 0.8446\n",
            "Epoch 11/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3684 - accuracy: 0.8496 - val_loss: 0.3704 - val_accuracy: 0.8525\n",
            "Epoch 12/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3669 - accuracy: 0.8552 - val_loss: 0.3677 - val_accuracy: 0.8529\n",
            "Epoch 13/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3666 - accuracy: 0.8520 - val_loss: 0.3712 - val_accuracy: 0.8529\n",
            "Epoch 14/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3655 - accuracy: 0.8562 - val_loss: 0.3653 - val_accuracy: 0.8575\n",
            "Epoch 15/50\n",
            "224/224 [==============================] - 1s 3ms/step - loss: 0.3643 - accuracy: 0.8527 - val_loss: 0.3787 - val_accuracy: 0.8458\n",
            "Epoch 16/50\n",
            "224/224 [==============================] - 1s 3ms/step - loss: 0.3630 - accuracy: 0.8602 - val_loss: 0.3649 - val_accuracy: 0.8542\n",
            "Epoch 17/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3609 - accuracy: 0.8566 - val_loss: 0.3653 - val_accuracy: 0.8533\n",
            "Epoch 18/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3573 - accuracy: 0.8554 - val_loss: 0.3678 - val_accuracy: 0.8579\n",
            "Epoch 19/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3630 - accuracy: 0.8552 - val_loss: 0.3687 - val_accuracy: 0.8492\n",
            "Epoch 20/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3579 - accuracy: 0.8571 - val_loss: 0.3762 - val_accuracy: 0.8421\n",
            "Epoch 21/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3606 - accuracy: 0.8605 - val_loss: 0.3642 - val_accuracy: 0.8554\n",
            "Epoch 22/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3541 - accuracy: 0.8568 - val_loss: 0.3641 - val_accuracy: 0.8558\n",
            "Epoch 23/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3568 - accuracy: 0.8612 - val_loss: 0.3706 - val_accuracy: 0.8521\n",
            "Epoch 24/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3508 - accuracy: 0.8596 - val_loss: 0.3671 - val_accuracy: 0.8521\n",
            "Epoch 25/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3511 - accuracy: 0.8598 - val_loss: 0.3662 - val_accuracy: 0.8529\n",
            "Epoch 26/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3536 - accuracy: 0.8554 - val_loss: 0.3674 - val_accuracy: 0.8521\n",
            "Epoch 27/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3509 - accuracy: 0.8557 - val_loss: 0.3650 - val_accuracy: 0.8529\n",
            "Epoch 28/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3523 - accuracy: 0.8605 - val_loss: 0.3648 - val_accuracy: 0.8612\n",
            "Epoch 29/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3542 - accuracy: 0.8596 - val_loss: 0.3571 - val_accuracy: 0.8587\n",
            "Epoch 30/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3489 - accuracy: 0.8584 - val_loss: 0.3611 - val_accuracy: 0.8567\n",
            "Epoch 31/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3455 - accuracy: 0.8609 - val_loss: 0.3633 - val_accuracy: 0.8558\n",
            "Epoch 32/50\n",
            "224/224 [==============================] - 1s 5ms/step - loss: 0.3487 - accuracy: 0.8605 - val_loss: 0.3559 - val_accuracy: 0.8612\n",
            "Epoch 33/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3466 - accuracy: 0.8609 - val_loss: 0.3562 - val_accuracy: 0.8587\n",
            "Epoch 34/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3472 - accuracy: 0.8621 - val_loss: 0.3631 - val_accuracy: 0.8554\n",
            "Epoch 35/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3472 - accuracy: 0.8616 - val_loss: 0.3564 - val_accuracy: 0.8592\n",
            "Epoch 36/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3477 - accuracy: 0.8614 - val_loss: 0.3606 - val_accuracy: 0.8558\n",
            "Epoch 37/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3460 - accuracy: 0.8648 - val_loss: 0.3630 - val_accuracy: 0.8517\n",
            "Epoch 38/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3473 - accuracy: 0.8584 - val_loss: 0.3632 - val_accuracy: 0.8525\n",
            "Epoch 39/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3518 - accuracy: 0.8598 - val_loss: 0.3558 - val_accuracy: 0.8617\n",
            "Epoch 40/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3502 - accuracy: 0.8600 - val_loss: 0.3565 - val_accuracy: 0.8608\n",
            "Epoch 41/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3461 - accuracy: 0.8614 - val_loss: 0.3589 - val_accuracy: 0.8533\n",
            "Epoch 42/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3461 - accuracy: 0.8582 - val_loss: 0.3568 - val_accuracy: 0.8604\n",
            "Epoch 43/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3432 - accuracy: 0.8568 - val_loss: 0.3624 - val_accuracy: 0.8508\n",
            "Epoch 44/50\n",
            "224/224 [==============================] - 1s 3ms/step - loss: 0.3414 - accuracy: 0.8620 - val_loss: 0.3581 - val_accuracy: 0.8583\n",
            "Epoch 45/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3437 - accuracy: 0.8650 - val_loss: 0.3571 - val_accuracy: 0.8571\n",
            "Epoch 46/50\n",
            "224/224 [==============================] - 1s 4ms/step - loss: 0.3425 - accuracy: 0.8614 - val_loss: 0.3556 - val_accuracy: 0.8546\n",
            "Epoch 47/50\n",
            "224/224 [==============================] - 1s 6ms/step - loss: 0.3412 - accuracy: 0.8645 - val_loss: 0.3541 - val_accuracy: 0.8579\n",
            "Epoch 48/50\n",
            "224/224 [==============================] - 2s 7ms/step - loss: 0.3419 - accuracy: 0.8614 - val_loss: 0.3548 - val_accuracy: 0.8567\n",
            "Epoch 49/50\n",
            "224/224 [==============================] - 1s 7ms/step - loss: 0.3434 - accuracy: 0.8604 - val_loss: 0.3563 - val_accuracy: 0.8571\n",
            "Epoch 50/50\n",
            "224/224 [==============================] - 2s 7ms/step - loss: 0.3404 - accuracy: 0.8604 - val_loss: 0.3564 - val_accuracy: 0.8587\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (12, 6))\n",
        "\n",
        "train_loss = anntunned_history.history['loss']\n",
        "val_loss = anntunned_history.history['val_loss'] \n",
        "epoch = range(1, 51)\n",
        "sns.lineplot(epoch, train_loss, label = 'Training Loss')\n",
        "sns.lineplot(epoch, val_loss, label = 'Validation Loss')\n",
        "plt.title('Training and Validation Loss\\n')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "AaU-1-B9D0af",
        "outputId": "96974446-c2bf-4578-945a-ce618b77ad34"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (12, 6))\n",
        "\n",
        "train_loss = anntunned_history.history['accuracy']\n",
        "val_loss = anntunned_history.history['val_accuracy'] \n",
        "epoch = range(1,51)\n",
        "sns.lineplot(epoch, train_loss, label = 'Training accuracy')\n",
        "sns.lineplot(epoch, val_loss, label = 'Validation accuracy')\n",
        "plt.title('Training and Validation Accuracy\\n')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "upGctddOD56D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "8b5002c0-f033-4903-d5b0-a44b8d2fb1bf"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicting the Test set results\n",
        "y_pred = model_tunned.predict(x_test)\n",
        "y_pred = (y_pred > 0.5)\n",
        "# Calculate the Accuracy\n",
        "accuracy = accuracy_score(y_pred,y_test)"
      ],
      "metadata": {
        "id": "vNdnCfF8OhNV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ced29e48-bbd2-46ea-f313-59877b0223ec"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)"
      ],
      "metadata": {
        "id": "YUWUtgdCtVdt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac0de6a7-8005-47d5-f13a-cfdc3a237421"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1535   60]\n",
            " [ 207  198]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy after tuning the FFNN:', (accuracy)*100,'%')"
      ],
      "metadata": {
        "id": "NnyELeYGOBGP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4ff4b6c-3162-4e48-c21e-b3198b0d16e9"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy after tuning the FFNN: 86.65 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, mean_squared_error, classification_report\n",
        "accuracy = round(accuracy_score(y_test, y_pred) * 100, 2)\n",
        "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
        "report = classification_report(y_test, y_pred)\n",
        "print(\"The Classification report is as follows::\\n\", report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m_oRdvpQucRM",
        "outputId": "ab624b40-f006-445f-8dba-23dafb917455"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Classification report is as follows::\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.96      0.92      1595\n",
            "           1       0.77      0.49      0.60       405\n",
            "\n",
            "    accuracy                           0.87      2000\n",
            "   macro avg       0.82      0.73      0.76      2000\n",
            "weighted avg       0.86      0.87      0.85      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize = (10, 8))\n",
        "sns.heatmap(cm, annot = True, fmt = '.0f', linewidths = .5, square = True)\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.title('Accuracy: {0}'.format(round(accuracy, 2)))\n",
        "plt.ylabel('Actual labels')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "e_UN6mBDueAY",
        "outputId": "c6637e12-1a05-4ab4-c40f-7d6660906e1a"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}