ANN_training.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOQpzgYyQF4COhuOTbezJ+L",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/glitcher007/NLP/blob/main/ANN_training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5OYylbD0QBe",
        "outputId": "78dbd736-12cc-46a9-8bf4-e90b78fe787b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.23.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.48.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.23.5)\n",
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            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.1)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
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            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.4)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.23.5)\n",
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            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.2.0)\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (2.15.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install numpy\n",
        "!pip install matplotlib\n",
        "!pip install pandas\n",
        "!pip install scikit-learn\n",
        "!pip install keras\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Artificial Neural Network\n",
        "\n",
        "\n",
        "# Part 1 - Data Preprocessing\n",
        "\n",
        "# Importing the libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Importing the dataset\n",
        "dataset = pd.read_csv('/content/Churn_Modelling (1).csv')\n",
        "X = dataset.iloc[:, 3:13]\n",
        "y = dataset.iloc[:, 13]\n",
        "\n",
        "#Create dummy variables\n",
        "geography=pd.get_dummies(X[\"Geography\"],drop_first=True)\n",
        "gender=pd.get_dummies(X['Gender'],drop_first=True)\n",
        "\n",
        "## Concatenate the Data Frames\n",
        "\n",
        "X=pd.concat([X,geography,gender],axis=1)\n",
        "\n",
        "## Drop Unnecessary columns\n",
        "X=X.drop(['Geography','Gender'],axis=1)\n",
        "\n",
        "# Splitting the dataset into the Training set and Test set\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)\n",
        "\n",
        "# Feature Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)\n",
        "\n",
        "# Part 2 - Now let's make the ANN!\n",
        "\n",
        "# Importing the Keras libraries and packages\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",
        "\n",
        "# Initialising the ANN\n",
        "classifier = Sequential()\n",
        "\n",
        "# Adding the input layer and the first hidden layer\n",
        "# Adding the input layer and the first hidden layer\n",
        "\n",
        "classifier.add(Dense(units = 6, kernel_initializer = 'he_uniform', activation = 'relu', input_dim = 11))\n",
        "\n",
        "classifier.add(Dropout(0.3))\n",
        "# Adding the second hidden layer\n",
        "classifier.add(Dense(units = 6, kernel_initializer = 'he_uniform', activation = 'relu'))\n",
        "# Adding the output layer\n",
        "classifier.add(Dropout(0.3))\n",
        "classifier.add(Dense(units = 1, kernel_initializer = 'glorot_uniform', activation = 'sigmoid'))\n",
        "classifier.compile(optimizer = 'Adamax', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
        "\n",
        "# Fitting the ANN to the Training setmodel_history = classifier.fit(X_train, y_train, validation_split=0.33, batch_size=10, epochs=100)\n",
        "model_history = classifier.fit(X_train, y_train, validation_split=0.33, batch_size=10, epochs=100)\n",
        "\n",
        "# list all data in history\n",
        "\n",
        "print(model_history.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(model_history.history['accuracy'])\n",
        "plt.plot(model_history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# summarize history for loss\n",
        "plt.plot(model_history.history['loss'])\n",
        "plt.plot(model_history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Part 3 - Making the predictions and evaluating the model\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "y_pred = (y_pred > 0.5)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Calculate the Accuracy\n",
        "from sklearn.metrics import accuracy_score\n",
        "score=accuracy_score(y_pred,y_test)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "g7E2mTDS1-Q5",
        "outputId": "4fafc907-806c-4e83-b5f8-7df654005a71"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.7086 - accuracy: 0.6874 - val_loss: 0.5881 - val_accuracy: 0.7713\n",
            "Epoch 2/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.6051 - accuracy: 0.7528 - val_loss: 0.5382 - val_accuracy: 0.7952\n",
            "Epoch 3/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.5506 - accuracy: 0.7809 - val_loss: 0.5148 - val_accuracy: 0.7955\n",
            "Epoch 4/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.5326 - accuracy: 0.7893 - val_loss: 0.5017 - val_accuracy: 0.7955\n",
            "Epoch 5/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.5126 - accuracy: 0.7959 - val_loss: 0.4914 - val_accuracy: 0.7955\n",
            "Epoch 6/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.5043 - accuracy: 0.7960 - val_loss: 0.4833 - val_accuracy: 0.7955\n",
            "Epoch 7/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4981 - accuracy: 0.7974 - val_loss: 0.4767 - val_accuracy: 0.7963\n",
            "Epoch 8/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4948 - accuracy: 0.7960 - val_loss: 0.4712 - val_accuracy: 0.7963\n",
            "Epoch 9/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4840 - accuracy: 0.8009 - val_loss: 0.4651 - val_accuracy: 0.7970\n",
            "Epoch 10/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4814 - accuracy: 0.8035 - val_loss: 0.4608 - val_accuracy: 0.7989\n",
            "Epoch 11/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4776 - accuracy: 0.8013 - val_loss: 0.4575 - val_accuracy: 0.7993\n",
            "Epoch 12/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4747 - accuracy: 0.8031 - val_loss: 0.4541 - val_accuracy: 0.7997\n",
            "Epoch 13/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4701 - accuracy: 0.8031 - val_loss: 0.4509 - val_accuracy: 0.8016\n",
            "Epoch 14/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4690 - accuracy: 0.8041 - val_loss: 0.4486 - val_accuracy: 0.8023\n",
            "Epoch 15/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4678 - accuracy: 0.8054 - val_loss: 0.4461 - val_accuracy: 0.8035\n",
            "Epoch 16/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4724 - accuracy: 0.8000 - val_loss: 0.4453 - val_accuracy: 0.8023\n",
            "Epoch 17/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4618 - accuracy: 0.8043 - val_loss: 0.4428 - val_accuracy: 0.8020\n",
            "Epoch 18/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4588 - accuracy: 0.8069 - val_loss: 0.4407 - val_accuracy: 0.8035\n",
            "Epoch 19/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4610 - accuracy: 0.8044 - val_loss: 0.4393 - val_accuracy: 0.8050\n",
            "Epoch 20/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4622 - accuracy: 0.8029 - val_loss: 0.4383 - val_accuracy: 0.8054\n",
            "Epoch 21/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4566 - accuracy: 0.8057 - val_loss: 0.4371 - val_accuracy: 0.8054\n",
            "Epoch 22/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4526 - accuracy: 0.8115 - val_loss: 0.4351 - val_accuracy: 0.8069\n",
            "Epoch 23/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4498 - accuracy: 0.8069 - val_loss: 0.4338 - val_accuracy: 0.8069\n",
            "Epoch 24/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4470 - accuracy: 0.8102 - val_loss: 0.4327 - val_accuracy: 0.8065\n",
            "Epoch 25/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4530 - accuracy: 0.8076 - val_loss: 0.4320 - val_accuracy: 0.8061\n",
            "Epoch 26/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4517 - accuracy: 0.8029 - val_loss: 0.4309 - val_accuracy: 0.8061\n",
            "Epoch 27/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4505 - accuracy: 0.8063 - val_loss: 0.4308 - val_accuracy: 0.8065\n",
            "Epoch 28/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4505 - accuracy: 0.8056 - val_loss: 0.4302 - val_accuracy: 0.8069\n",
            "Epoch 29/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4463 - accuracy: 0.8089 - val_loss: 0.4294 - val_accuracy: 0.8069\n",
            "Epoch 30/100\n",
            "536/536 [==============================] - 2s 5ms/step - loss: 0.4509 - accuracy: 0.8072 - val_loss: 0.4292 - val_accuracy: 0.8069\n",
            "Epoch 31/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4415 - accuracy: 0.8095 - val_loss: 0.4276 - val_accuracy: 0.8076\n",
            "Epoch 32/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4451 - accuracy: 0.8057 - val_loss: 0.4266 - val_accuracy: 0.8084\n",
            "Epoch 33/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4484 - accuracy: 0.8029 - val_loss: 0.4272 - val_accuracy: 0.8069\n",
            "Epoch 34/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4432 - accuracy: 0.8095 - val_loss: 0.4263 - val_accuracy: 0.8076\n",
            "Epoch 35/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4428 - accuracy: 0.8119 - val_loss: 0.4257 - val_accuracy: 0.8076\n",
            "Epoch 36/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4422 - accuracy: 0.8087 - val_loss: 0.4252 - val_accuracy: 0.8073\n",
            "Epoch 37/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4405 - accuracy: 0.8097 - val_loss: 0.4248 - val_accuracy: 0.8076\n",
            "Epoch 38/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4445 - accuracy: 0.8057 - val_loss: 0.4250 - val_accuracy: 0.8065\n",
            "Epoch 39/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4455 - accuracy: 0.8089 - val_loss: 0.4245 - val_accuracy: 0.8069\n",
            "Epoch 40/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4368 - accuracy: 0.8071 - val_loss: 0.4236 - val_accuracy: 0.8076\n",
            "Epoch 41/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4374 - accuracy: 0.8056 - val_loss: 0.4234 - val_accuracy: 0.8061\n",
            "Epoch 42/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4365 - accuracy: 0.8095 - val_loss: 0.4225 - val_accuracy: 0.8069\n",
            "Epoch 43/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4354 - accuracy: 0.8084 - val_loss: 0.4218 - val_accuracy: 0.8080\n",
            "Epoch 44/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4395 - accuracy: 0.8056 - val_loss: 0.4225 - val_accuracy: 0.8046\n",
            "Epoch 45/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4387 - accuracy: 0.8078 - val_loss: 0.4218 - val_accuracy: 0.8069\n",
            "Epoch 46/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4373 - accuracy: 0.8087 - val_loss: 0.4214 - val_accuracy: 0.8065\n",
            "Epoch 47/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4330 - accuracy: 0.8080 - val_loss: 0.4208 - val_accuracy: 0.8061\n",
            "Epoch 48/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4398 - accuracy: 0.8087 - val_loss: 0.4210 - val_accuracy: 0.8054\n",
            "Epoch 49/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4419 - accuracy: 0.8033 - val_loss: 0.4215 - val_accuracy: 0.8031\n",
            "Epoch 50/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4307 - accuracy: 0.8127 - val_loss: 0.4198 - val_accuracy: 0.8061\n",
            "Epoch 51/100\n",
            "536/536 [==============================] - 2s 5ms/step - loss: 0.4378 - accuracy: 0.8104 - val_loss: 0.4197 - val_accuracy: 0.8061\n",
            "Epoch 52/100\n",
            "536/536 [==============================] - 3s 5ms/step - loss: 0.4346 - accuracy: 0.8104 - val_loss: 0.4191 - val_accuracy: 0.8069\n",
            "Epoch 53/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4348 - accuracy: 0.8121 - val_loss: 0.4189 - val_accuracy: 0.8080\n",
            "Epoch 54/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4361 - accuracy: 0.8097 - val_loss: 0.4186 - val_accuracy: 0.8080\n",
            "Epoch 55/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4374 - accuracy: 0.8097 - val_loss: 0.4194 - val_accuracy: 0.8061\n",
            "Epoch 56/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4319 - accuracy: 0.8121 - val_loss: 0.4189 - val_accuracy: 0.8069\n",
            "Epoch 57/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4298 - accuracy: 0.8121 - val_loss: 0.4181 - val_accuracy: 0.8076\n",
            "Epoch 58/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4299 - accuracy: 0.8119 - val_loss: 0.4178 - val_accuracy: 0.8073\n",
            "Epoch 59/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4312 - accuracy: 0.8063 - val_loss: 0.4169 - val_accuracy: 0.8095\n",
            "Epoch 60/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4331 - accuracy: 0.8127 - val_loss: 0.4172 - val_accuracy: 0.8088\n",
            "Epoch 61/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4349 - accuracy: 0.8080 - val_loss: 0.4173 - val_accuracy: 0.8076\n",
            "Epoch 62/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4308 - accuracy: 0.8104 - val_loss: 0.4172 - val_accuracy: 0.8076\n",
            "Epoch 63/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4321 - accuracy: 0.8132 - val_loss: 0.4171 - val_accuracy: 0.8076\n",
            "Epoch 64/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4294 - accuracy: 0.8108 - val_loss: 0.4171 - val_accuracy: 0.8058\n",
            "Epoch 65/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4321 - accuracy: 0.8067 - val_loss: 0.4171 - val_accuracy: 0.8042\n",
            "Epoch 66/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4340 - accuracy: 0.8121 - val_loss: 0.4170 - val_accuracy: 0.8050\n",
            "Epoch 67/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4281 - accuracy: 0.8134 - val_loss: 0.4163 - val_accuracy: 0.8069\n",
            "Epoch 68/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4345 - accuracy: 0.8069 - val_loss: 0.4164 - val_accuracy: 0.8035\n",
            "Epoch 69/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4379 - accuracy: 0.8046 - val_loss: 0.4166 - val_accuracy: 0.8027\n",
            "Epoch 70/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4295 - accuracy: 0.8115 - val_loss: 0.4163 - val_accuracy: 0.8027\n",
            "Epoch 71/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4256 - accuracy: 0.8069 - val_loss: 0.4157 - val_accuracy: 0.8042\n",
            "Epoch 72/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4272 - accuracy: 0.8123 - val_loss: 0.4148 - val_accuracy: 0.8076\n",
            "Epoch 73/100\n",
            "536/536 [==============================] - 1s 3ms/step - loss: 0.4322 - accuracy: 0.8078 - val_loss: 0.4155 - val_accuracy: 0.8023\n",
            "Epoch 74/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4310 - accuracy: 0.8095 - val_loss: 0.4151 - val_accuracy: 0.8058\n",
            "Epoch 75/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4266 - accuracy: 0.8097 - val_loss: 0.4147 - val_accuracy: 0.8031\n",
            "Epoch 76/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4254 - accuracy: 0.8166 - val_loss: 0.4140 - val_accuracy: 0.8080\n",
            "Epoch 77/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4272 - accuracy: 0.8099 - val_loss: 0.4138 - val_accuracy: 0.8084\n",
            "Epoch 78/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4273 - accuracy: 0.8149 - val_loss: 0.4134 - val_accuracy: 0.8095\n",
            "Epoch 79/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4284 - accuracy: 0.8123 - val_loss: 0.4135 - val_accuracy: 0.8080\n",
            "Epoch 80/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4229 - accuracy: 0.8162 - val_loss: 0.4126 - val_accuracy: 0.8103\n",
            "Epoch 81/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4301 - accuracy: 0.8119 - val_loss: 0.4124 - val_accuracy: 0.8099\n",
            "Epoch 82/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4303 - accuracy: 0.8078 - val_loss: 0.4129 - val_accuracy: 0.8088\n",
            "Epoch 83/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4295 - accuracy: 0.8121 - val_loss: 0.4129 - val_accuracy: 0.8065\n",
            "Epoch 84/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4274 - accuracy: 0.8110 - val_loss: 0.4126 - val_accuracy: 0.8103\n",
            "Epoch 85/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4283 - accuracy: 0.8074 - val_loss: 0.4124 - val_accuracy: 0.8076\n",
            "Epoch 86/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4279 - accuracy: 0.8128 - val_loss: 0.4123 - val_accuracy: 0.8073\n",
            "Epoch 87/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4262 - accuracy: 0.8153 - val_loss: 0.4121 - val_accuracy: 0.8092\n",
            "Epoch 88/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4270 - accuracy: 0.8121 - val_loss: 0.4120 - val_accuracy: 0.8095\n",
            "Epoch 89/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4282 - accuracy: 0.8080 - val_loss: 0.4119 - val_accuracy: 0.8084\n",
            "Epoch 90/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4237 - accuracy: 0.8125 - val_loss: 0.4119 - val_accuracy: 0.8069\n",
            "Epoch 91/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4314 - accuracy: 0.8113 - val_loss: 0.4123 - val_accuracy: 0.8054\n",
            "Epoch 92/100\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4164 - accuracy: 0.8166 - val_loss: 0.4112 - val_accuracy: 0.8095\n",
            "Epoch 93/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4278 - accuracy: 0.8102 - val_loss: 0.4110 - val_accuracy: 0.8095\n",
            "Epoch 94/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4262 - accuracy: 0.8138 - val_loss: 0.4109 - val_accuracy: 0.8107\n",
            "Epoch 95/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4277 - accuracy: 0.8156 - val_loss: 0.4111 - val_accuracy: 0.8133\n",
            "Epoch 96/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4225 - accuracy: 0.8188 - val_loss: 0.4107 - val_accuracy: 0.8129\n",
            "Epoch 97/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4232 - accuracy: 0.8164 - val_loss: 0.4106 - val_accuracy: 0.8137\n",
            "Epoch 98/100\n",
            "536/536 [==============================] - 1s 2ms/step - loss: 0.4241 - accuracy: 0.8149 - val_loss: 0.4105 - val_accuracy: 0.8118\n",
            "Epoch 99/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4201 - accuracy: 0.8143 - val_loss: 0.4097 - val_accuracy: 0.8137\n",
            "Epoch 100/100\n",
            "536/536 [==============================] - 2s 3ms/step - loss: 0.4186 - accuracy: 0.8151 - val_loss: 0.4093 - val_accuracy: 0.8133\n",
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 1ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "t24lc3nX2W1G"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}