8_feature_scaling.ipynb

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        "<a href=\"https://colab.research.google.com/github/singhsourav0/Deep-Learning-Odyssey/blob/main/8_feature_scaling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "<h1><font color=\"cyan\">Q:- Why we need to use Feature Scaling in my model?</font></h1>\n",
        "<h4><font color=\"teal\">In any ANN model if the features are scaled diffrent(Unnormalized Feature) means one input column are not related to other column then to train our model is very hard so we scaled the input column with Standardization or Normalisation(input scaling).</br>\n",
        "lets understand with examples if we have 2 input column 1 is age and the other is salary then the range of 2nd column is so wide and not related to column 1. </br>\n",
        "In this case when we try to train our model then it will stuck in specific range of loss.\n",
        "and stop reducing the loss and for overcome from this issue we use feature scaling.</br>\n",
        "Now lets break the concept to understand when we use Standardization or normalisation.</font></h4>\n",
        "\n",
        "1. <h3><font color=\"yellow\"><b>Standardization (Z-score normalization):</b></font>\n",
        "\n",
        "   Standardization scales the features such that they have a mean of 0 and a standard deviation of 1. It transforms the data distribution to have a mean of 0 and a standard deviation of 1.\n",
        "\n",
        "   By standardizing the features, we ensure that they have similar scales, which can help algorithms converge faster and make them less sensitive to the scale of features. the range of data  after scaled is -1 t0 +1.\n",
        "\n",
        "2. <h3><font color=\"yellow\"><b>Normalization (Min-Max Scaling):</b></font>\n",
        "\n",
        "   Normalization scales the features to a fixed range, typically between 0 and 1. It preserves the relative relationships between values in the original data.\n",
        "\n",
        "   Normalization is useful when the features have different ranges and we want to scale them to a common range, typically 0 to 1. It's beneficial for algorithms that require features to be on the same scale.\n",
        "\n",
        "Both standardization and normalization are beneficial for various machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), and neural networks.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/singhsourav0/Deep-Learning-Odyssey/main/rrrr.jpg\" width=\"700\" height=\"300\">\n",
        "\n",
        " <h4><font color=\"teal\">sourav kumar(singhsourav0)</font></h4>"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
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      "source": [
        "!wget"
      ],
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        "df = pd.read_csv('/content/Social_Network_Ads.csv')"
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        "df = df.iloc[:,2:]\n",
        "df.head()"
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              "   Age  EstimatedSalary  Purchased\n",
              "0   19            19000          0\n",
              "1   35            20000          0\n",
              "2   26            43000          0\n",
              "3   27            57000          0\n",
              "4   19            76000          0"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 400,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 18,\n        \"max\": 60,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          50,\n          39,\n          38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EstimatedSalary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34096,\n        \"min\": 15000,\n        \"max\": 150000,\n        \"num_unique_values\": 117,\n        \"samples\": [\n          117000,\n          76000,\n          88000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Purchased\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        "import seaborn as sns"
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      "execution_count": 6,
      "outputs": []
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        "sns.scatterplot(x=df.iloc[:,0], y=df.iloc[:,1])\n",
        "# sns.scatterplot(df.iloc[:,0],df.iloc[:,1])"
      ],
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          "height": 466
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        "id": "ZnBaOdwl7U2y",
        "outputId": "b3b90f55-ccfd-4a28-93a0-fe1571f6321b"
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      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age', ylabel='EstimatedSalary'>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:,0:2]\n",
        "y = df.iloc[:,-1]"
      ],
      "metadata": {
        "id": "z6bzi-427cKi"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
      ],
      "metadata": {
        "id": "YSPIQzY97i_C"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import Sequential\n",
        "from keras.layers import Dense"
      ],
      "metadata": {
        "id": "Y32nU_KD7udn"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(128,activation='relu',input_dim=2))\n",
        "model.add(Dense(1,activation='sigmoid'))"
      ],
      "metadata": {
        "id": "KdFmkN8h745Y"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6xRTNvI9GMq",
        "outputId": "2b3a54a7-84d4-4fc7-b6ee-bd22a1e019aa"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 128)               384       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 513 (2.00 KB)\n",
            "Trainable params: 513 (2.00 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "uvLbTt9S_ByW"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZaKyBN1B_LTa",
        "outputId": "c74a107a-c94d-4e20-9203-9c8719b68e6c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "10/10 [==============================] - 1s 30ms/step - loss: 405.6186 - accuracy: 0.4781 - val_loss: 354.4272 - val_accuracy: 0.6000\n",
            "Epoch 2/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 166.0576 - accuracy: 0.4906 - val_loss: 124.0222 - val_accuracy: 0.6000\n",
            "Epoch 3/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 141.6576 - accuracy: 0.5031 - val_loss: 4.9716 - val_accuracy: 0.6500\n",
            "Epoch 4/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 62.2307 - accuracy: 0.5219 - val_loss: 134.4708 - val_accuracy: 0.6000\n",
            "Epoch 5/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 62.5766 - accuracy: 0.5594 - val_loss: 29.8704 - val_accuracy: 0.4000\n",
            "Epoch 6/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 32.7961 - accuracy: 0.5437 - val_loss: 53.0586 - val_accuracy: 0.6000\n",
            "Epoch 7/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 48.3598 - accuracy: 0.5406 - val_loss: 60.9723 - val_accuracy: 0.4000\n",
            "Epoch 8/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 54.7022 - accuracy: 0.4781 - val_loss: 79.5773 - val_accuracy: 0.6000\n",
            "Epoch 9/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 79.8064 - accuracy: 0.5281 - val_loss: 42.3792 - val_accuracy: 0.6000\n",
            "Epoch 10/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 82.5321 - accuracy: 0.5094 - val_loss: 64.3888 - val_accuracy: 0.4000\n",
            "Epoch 11/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 107.9277 - accuracy: 0.5281 - val_loss: 73.9799 - val_accuracy: 0.4000\n",
            "Epoch 12/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 64.3187 - accuracy: 0.4156 - val_loss: 42.4499 - val_accuracy: 0.6000\n",
            "Epoch 13/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 70.4661 - accuracy: 0.5406 - val_loss: 86.0902 - val_accuracy: 0.4000\n",
            "Epoch 14/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 92.9953 - accuracy: 0.5031 - val_loss: 109.7237 - val_accuracy: 0.4000\n",
            "Epoch 15/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 79.5958 - accuracy: 0.5219 - val_loss: 47.1565 - val_accuracy: 0.4000\n",
            "Epoch 16/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 25.7183 - accuracy: 0.4906 - val_loss: 48.7817 - val_accuracy: 0.6000\n",
            "Epoch 17/100\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 47.6809 - accuracy: 0.5781 - val_loss: 6.2750 - val_accuracy: 0.4000\n",
            "Epoch 18/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 28.8008 - accuracy: 0.5156 - val_loss: 89.0938 - val_accuracy: 0.4000\n",
            "Epoch 19/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 85.8994 - accuracy: 0.5031 - val_loss: 102.3546 - val_accuracy: 0.4000\n",
            "Epoch 20/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 98.1406 - accuracy: 0.4719 - val_loss: 104.1714 - val_accuracy: 0.4000\n",
            "Epoch 21/100\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 79.2097 - accuracy: 0.5094 - val_loss: 19.2345 - val_accuracy: 0.4000\n",
            "Epoch 22/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 94.8034 - accuracy: 0.5469 - val_loss: 145.2439 - val_accuracy: 0.4000\n",
            "Epoch 23/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 111.4803 - accuracy: 0.5781 - val_loss: 212.7355 - val_accuracy: 0.4000\n",
            "Epoch 24/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 192.1388 - accuracy: 0.4781 - val_loss: 232.6330 - val_accuracy: 0.6000\n",
            "Epoch 25/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 176.2047 - accuracy: 0.4781 - val_loss: 185.0430 - val_accuracy: 0.6000\n",
            "Epoch 26/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 134.8848 - accuracy: 0.6156 - val_loss: 191.0497 - val_accuracy: 0.4000\n",
            "Epoch 27/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 116.3028 - accuracy: 0.5531 - val_loss: 124.1851 - val_accuracy: 0.4000\n",
            "Epoch 28/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 108.9647 - accuracy: 0.5125 - val_loss: 65.0161 - val_accuracy: 0.4000\n",
            "Epoch 29/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 40.1189 - accuracy: 0.5406 - val_loss: 20.1244 - val_accuracy: 0.6000\n",
            "Epoch 30/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 35.1767 - accuracy: 0.5219 - val_loss: 87.1847 - val_accuracy: 0.4000\n",
            "Epoch 31/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 34.9300 - accuracy: 0.5281 - val_loss: 1.4059 - val_accuracy: 0.6375\n",
            "Epoch 32/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 21.9807 - accuracy: 0.5531 - val_loss: 31.6107 - val_accuracy: 0.4000\n",
            "Epoch 33/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 38.5050 - accuracy: 0.5406 - val_loss: 0.9418 - val_accuracy: 0.5250\n",
            "Epoch 34/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 28.3024 - accuracy: 0.5094 - val_loss: 42.4423 - val_accuracy: 0.6000\n",
            "Epoch 35/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 35.0066 - accuracy: 0.4969 - val_loss: 98.3201 - val_accuracy: 0.6000\n",
            "Epoch 36/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 51.2031 - accuracy: 0.5719 - val_loss: 56.9170 - val_accuracy: 0.4000\n",
            "Epoch 37/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 33.4666 - accuracy: 0.5469 - val_loss: 33.0808 - val_accuracy: 0.6000\n",
            "Epoch 38/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 19.6413 - accuracy: 0.5125 - val_loss: 14.6765 - val_accuracy: 0.4000\n",
            "Epoch 39/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 32.4338 - accuracy: 0.5000 - val_loss: 34.1867 - val_accuracy: 0.6000\n",
            "Epoch 40/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 25.5691 - accuracy: 0.5063 - val_loss: 48.3993 - val_accuracy: 0.4000\n",
            "Epoch 41/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 28.1419 - accuracy: 0.4938 - val_loss: 54.5306 - val_accuracy: 0.6000\n",
            "Epoch 42/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 40.3123 - accuracy: 0.6094 - val_loss: 48.1698 - val_accuracy: 0.4000\n",
            "Epoch 43/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 27.5710 - accuracy: 0.5531 - val_loss: 58.4531 - val_accuracy: 0.4000\n",
            "Epoch 44/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 54.0219 - accuracy: 0.4469 - val_loss: 72.5685 - val_accuracy: 0.6000\n",
            "Epoch 45/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 91.3520 - accuracy: 0.5781 - val_loss: 20.3291 - val_accuracy: 0.6000\n",
            "Epoch 46/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 26.8925 - accuracy: 0.5906 - val_loss: 46.5096 - val_accuracy: 0.6000\n",
            "Epoch 47/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 80.4841 - accuracy: 0.5594 - val_loss: 11.0068 - val_accuracy: 0.4000\n",
            "Epoch 48/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 64.2914 - accuracy: 0.4156 - val_loss: 57.6133 - val_accuracy: 0.4000\n",
            "Epoch 49/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 64.4547 - accuracy: 0.5281 - val_loss: 32.3757 - val_accuracy: 0.6000\n",
            "Epoch 50/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 28.4562 - accuracy: 0.4719 - val_loss: 41.4887 - val_accuracy: 0.6000\n",
            "Epoch 51/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 52.9196 - accuracy: 0.5156 - val_loss: 81.2346 - val_accuracy: 0.4000\n",
            "Epoch 52/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 47.1955 - accuracy: 0.4906 - val_loss: 67.5206 - val_accuracy: 0.6000\n",
            "Epoch 53/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 40.2208 - accuracy: 0.5594 - val_loss: 3.6950 - val_accuracy: 0.4000\n",
            "Epoch 54/100\n",
            "10/10 [==============================] - 0s 11ms/step - loss: 43.3949 - accuracy: 0.5156 - val_loss: 50.3082 - val_accuracy: 0.6000\n",
            "Epoch 55/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 83.2270 - accuracy: 0.5656 - val_loss: 103.6150 - val_accuracy: 0.6000\n",
            "Epoch 56/100\n",
            "10/10 [==============================] - 0s 11ms/step - loss: 117.1995 - accuracy: 0.4969 - val_loss: 174.5909 - val_accuracy: 0.6000\n",
            "Epoch 57/100\n",
            "10/10 [==============================] - 0s 11ms/step - loss: 74.1607 - accuracy: 0.5719 - val_loss: 49.5570 - val_accuracy: 0.6000\n",
            "Epoch 58/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 80.5030 - accuracy: 0.5406 - val_loss: 111.6952 - val_accuracy: 0.6000\n",
            "Epoch 59/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 58.1304 - accuracy: 0.5594 - val_loss: 37.9413 - val_accuracy: 0.4000\n",
            "Epoch 60/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 53.4172 - accuracy: 0.4844 - val_loss: 57.8126 - val_accuracy: 0.6000\n",
            "Epoch 61/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 37.4754 - accuracy: 0.5406 - val_loss: 46.8418 - val_accuracy: 0.6000\n",
            "Epoch 62/100\n",
            "10/10 [==============================] - 0s 11ms/step - loss: 25.6813 - accuracy: 0.5594 - val_loss: 71.1816 - val_accuracy: 0.6000\n",
            "Epoch 63/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 39.9700 - accuracy: 0.4469 - val_loss: 69.7220 - val_accuracy: 0.6000\n",
            "Epoch 64/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 53.0776 - accuracy: 0.5656 - val_loss: 12.2258 - val_accuracy: 0.4000\n",
            "Epoch 65/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 12.5396 - accuracy: 0.5781 - val_loss: 47.6472 - val_accuracy: 0.4000\n",
            "Epoch 66/100\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 26.0825 - accuracy: 0.4844 - val_loss: 4.3638 - val_accuracy: 0.4000\n",
            "Epoch 67/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 29.7473 - accuracy: 0.5531 - val_loss: 19.0923 - val_accuracy: 0.4000\n",
            "Epoch 68/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 59.6187 - accuracy: 0.4844 - val_loss: 46.0993 - val_accuracy: 0.6000\n",
            "Epoch 69/100\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 42.1596 - accuracy: 0.5094 - val_loss: 12.1816 - val_accuracy: 0.6000\n",
            "Epoch 70/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 13.7339 - accuracy: 0.5906 - val_loss: 14.0882 - val_accuracy: 0.6000\n",
            "Epoch 71/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 21.3157 - accuracy: 0.5437 - val_loss: 12.1176 - val_accuracy: 0.4000\n",
            "Epoch 72/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 36.7236 - accuracy: 0.5469 - val_loss: 92.2183 - val_accuracy: 0.4000\n",
            "Epoch 73/100\n",
            "10/10 [==============================] - 0s 11ms/step - loss: 64.5993 - accuracy: 0.4906 - val_loss: 46.0480 - val_accuracy: 0.6000\n",
            "Epoch 74/100\n",
            "10/10 [==============================] - 0s 11ms/step - loss: 47.8942 - accuracy: 0.5531 - val_loss: 42.5282 - val_accuracy: 0.4000\n",
            "Epoch 75/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 56.9366 - accuracy: 0.4969 - val_loss: 44.5064 - val_accuracy: 0.6000\n",
            "Epoch 76/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 19.5414 - accuracy: 0.5031 - val_loss: 24.9531 - val_accuracy: 0.4000\n",
            "Epoch 77/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 38.7220 - accuracy: 0.5156 - val_loss: 70.4806 - val_accuracy: 0.4000\n",
            "Epoch 78/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 32.8497 - accuracy: 0.5406 - val_loss: 67.4311 - val_accuracy: 0.4000\n",
            "Epoch 79/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 58.3747 - accuracy: 0.4531 - val_loss: 51.2630 - val_accuracy: 0.6000\n",
            "Epoch 80/100\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 53.3973 - accuracy: 0.5406 - val_loss: 5.7433 - val_accuracy: 0.4000\n",
            "Epoch 81/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 48.4858 - accuracy: 0.4719 - val_loss: 73.7793 - val_accuracy: 0.6000\n",
            "Epoch 82/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 87.7131 - accuracy: 0.5281 - val_loss: 90.7453 - val_accuracy: 0.6000\n",
            "Epoch 83/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 53.7430 - accuracy: 0.5406 - val_loss: 52.5316 - val_accuracy: 0.4000\n",
            "Epoch 84/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 36.2452 - accuracy: 0.5094 - val_loss: 5.1043 - val_accuracy: 0.4000\n",
            "Epoch 85/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 30.9377 - accuracy: 0.4719 - val_loss: 30.1326 - val_accuracy: 0.6000\n",
            "Epoch 86/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 31.6960 - accuracy: 0.4969 - val_loss: 32.8767 - val_accuracy: 0.6000\n",
            "Epoch 87/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 36.9135 - accuracy: 0.4906 - val_loss: 111.4323 - val_accuracy: 0.6000\n",
            "Epoch 88/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 94.7704 - accuracy: 0.4844 - val_loss: 210.7851 - val_accuracy: 0.6000\n",
            "Epoch 89/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 142.8130 - accuracy: 0.5531 - val_loss: 46.5913 - val_accuracy: 0.4000\n",
            "Epoch 90/100\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 90.1461 - accuracy: 0.5406 - val_loss: 27.7479 - val_accuracy: 0.4000\n",
            "Epoch 91/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 56.3132 - accuracy: 0.5281 - val_loss: 18.1873 - val_accuracy: 0.6000\n",
            "Epoch 92/100\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 42.5492 - accuracy: 0.5156 - val_loss: 4.2917 - val_accuracy: 0.6000\n",
            "Epoch 93/100\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 50.5301 - accuracy: 0.5781 - val_loss: 47.0554 - val_accuracy: 0.6000\n",
            "Epoch 94/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 59.6713 - accuracy: 0.4656 - val_loss: 68.8117 - val_accuracy: 0.4000\n",
            "Epoch 95/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 25.7629 - accuracy: 0.5500 - val_loss: 68.5962 - val_accuracy: 0.4000\n",
            "Epoch 96/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 55.9166 - accuracy: 0.4781 - val_loss: 32.3844 - val_accuracy: 0.6000\n",
            "Epoch 97/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 56.3855 - accuracy: 0.5656 - val_loss: 51.9977 - val_accuracy: 0.6000\n",
            "Epoch 98/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 51.2710 - accuracy: 0.4906 - val_loss: 68.7590 - val_accuracy: 0.4000\n",
            "Epoch 99/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 75.6538 - accuracy: 0.5281 - val_loss: 37.6709 - val_accuracy: 0.4000\n",
            "Epoch 100/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 100.0785 - accuracy: 0.5594 - val_loss: 134.9912 - val_accuracy: 0.4000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(history.history['val_accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "ZMQRl2lm_VHq",
        "outputId": "a547de84-966b-4032-ea13-e453cfdfca5d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x788b3aeaf3a0>]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Applying scaling"
      ],
      "metadata": {
        "id": "_STlGeh5_lBW"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        ":"
      ],
      "metadata": {
        "id": "toDKbJJ4coHN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "QQGb-bfy_5TK"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NRGauCO7KrVT",
        "outputId": "feadf43a-1699-47ab-bba8-ed140932051d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.88928823, -0.65924556],\n",
              "       [-0.17254846,  0.87392651],\n",
              "       [-1.04132394, -0.36440478],\n",
              "       [ 0.98581884,  0.6085698 ],\n",
              "       [-0.94479333,  0.57908572],\n",
              "       [ 0.40663519,  0.01888824],\n",
              "       [ 0.98581884,  2.11225779],\n",
              "       [ 0.31010458, -0.30543662],\n",
              "       [ 1.7580637 , -0.27595254],\n",
              "       [-0.17254846,  2.20071003],\n",
              "       [ 1.7580637 ,  1.0213469 ],\n",
              "       [-1.33091576, -1.48479975],\n",
              "       [ 2.04765553,  0.54960165],\n",
              "       [ 1.27541066,  1.90586924],\n",
              "       [-1.13785454,  0.31372902],\n",
              "       [-0.36560968, -0.77718187],\n",
              "       [-1.71703819,  0.49063349],\n",
              "       [-0.5586709 , -1.51428383],\n",
              "       [ 0.31010458, -0.71821372],\n",
              "       [ 0.02051275, -0.57079333],\n",
              "       [ 0.02051275,  0.04837232],\n",
              "       [-0.07601785, -0.51182517],\n",
              "       [-0.6552015 , -1.51428383],\n",
              "       [ 0.02051275,  0.31372902],\n",
              "       [ 0.31010458,  0.07785639],\n",
              "       [-0.46214029, -1.13099081],\n",
              "       [-0.75173211, -1.54376791],\n",
              "       [-0.26907907, -0.65924556],\n",
              "       [-1.13785454,  0.49063349],\n",
              "       [-0.07601785,  2.20071003],\n",
              "       [ 0.02051275,  0.04837232],\n",
              "       [-1.13785454, -1.57325199],\n",
              "       [ 1.08234944,  0.54960165],\n",
              "       [-0.26907907, -1.24892713],\n",
              "       [ 1.37194127, -0.92460227],\n",
              "       [-1.42744637, -1.21944305],\n",
              "       [-0.94479333, -0.95408634],\n",
              "       [ 1.95112492, -0.65924556],\n",
              "       [ 0.88928823, -0.57079333],\n",
              "       [-1.13785454,  0.31372902],\n",
              "       [ 0.02051275, -0.24646847],\n",
              "       [ 0.79275762, -1.39634752],\n",
              "       [-0.26907907, -0.36440478],\n",
              "       [ 0.88928823,  1.2867036 ],\n",
              "       [ 0.31010458, -0.18750031],\n",
              "       [-0.26907907, -0.57079333],\n",
              "       [-0.26907907, -1.39634752],\n",
              "       [ 1.46847188, -1.04253858],\n",
              "       [-0.07601785,  0.13682455],\n",
              "       [-0.84826272, -0.65924556],\n",
              "       [-0.07601785,  0.01888824],\n",
              "       [-0.26907907,  0.10734047],\n",
              "       [ 0.21357397, -0.30543662],\n",
              "       [-0.26907907,  0.28424494],\n",
              "       [ 0.11704336,  0.04837232],\n",
              "       [ 1.95112492,  2.20071003],\n",
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              "       [ 1.37194127,  0.6085698 ],\n",
              "       [-1.52397698, -1.51428383],\n",
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              "       [-1.13785454,  0.07785639],\n",
              "       [ 0.40663519, -0.45285701],\n",
              "       [-1.91009941, -0.7476978 ],\n",
              "       [ 0.40663519,  0.28424494],\n",
              "       [-0.26907907,  0.22527679],\n",
              "       [-0.5586709 ,  0.49063349],\n",
              "       [-0.36560968, -0.77718187],\n",
              "       [-0.26907907,  0.04837232],\n",
              "       [-0.07601785,  0.22527679],\n",
              "       [-0.26907907,  0.16630863],\n",
              "       [ 0.31010458, -0.54130925],\n",
              "       [ 0.79275762,  0.78547427],\n",
              "       [ 0.98581884,  2.02380556],\n",
              "       [-0.17254846, -0.27595254],\n",
              "       [-1.04132394, -0.45285701],\n",
              "       [-0.26907907, -0.3349207 ],\n",
              "       [ 0.11704336, -0.30543662],\n",
              "       [-0.26907907, -0.7476978 ],\n",
              "       [-0.07601785, -1.07202266],\n",
              "       [-0.26907907,  0.07785639],\n",
              "       [ 0.5031658 ,  1.75844885],\n",
              "       [-0.36560968,  1.25721952],\n",
              "       [ 0.79275762,  1.40463991],\n",
              "       [ 0.98581884, -0.83615003],\n",
              "       [-1.71703819, -1.36686344],\n",
              "       [-0.84826272,  0.31372902],\n",
              "       [-0.84826272,  2.31864634]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x=df.iloc[:,0], y=df.iloc[:,1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "CvhZdAbXKwoJ",
        "outputId": "da93d03b-990c-4fb0-81d4-fa312feb584e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age', ylabel='EstimatedSalary'>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(128,activation='relu',input_dim=2))\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(X_train_scaled,y_train,validation_data=(X_test_scaled,y_test),epochs=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g-DZoervAJzJ",
        "outputId": "3aae5484-ec11-4fa6-9504-41fb2a053644"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "10/10 [==============================] - 1s 30ms/step - loss: 0.6636 - accuracy: 0.7469 - val_loss: 0.6265 - val_accuracy: 0.8250\n",
            "Epoch 2/100\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 0.6107 - accuracy: 0.8281 - val_loss: 0.5740 - val_accuracy: 0.8000\n",
            "Epoch 3/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.5669 - accuracy: 0.8406 - val_loss: 0.5292 - val_accuracy: 0.8125\n",
            "Epoch 4/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.5264 - accuracy: 0.8438 - val_loss: 0.4919 - val_accuracy: 0.8125\n",
            "Epoch 5/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.4933 - accuracy: 0.8500 - val_loss: 0.4603 - val_accuracy: 0.8125\n",
            "Epoch 6/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.4647 - accuracy: 0.8469 - val_loss: 0.4326 - val_accuracy: 0.8125\n",
            "Epoch 7/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.4394 - accuracy: 0.8469 - val_loss: 0.4100 - val_accuracy: 0.8125\n",
            "Epoch 8/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 0.4170 - accuracy: 0.8500 - val_loss: 0.3892 - val_accuracy: 0.8250\n",
            "Epoch 9/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.3968 - accuracy: 0.8562 - val_loss: 0.3740 - val_accuracy: 0.8250\n",
            "Epoch 10/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.3798 - accuracy: 0.8625 - val_loss: 0.3602 - val_accuracy: 0.8375\n",
            "Epoch 11/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.3646 - accuracy: 0.8750 - val_loss: 0.3484 - val_accuracy: 0.8375\n",
            "Epoch 12/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.3511 - accuracy: 0.8750 - val_loss: 0.3389 - val_accuracy: 0.8375\n",
            "Epoch 13/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.3393 - accuracy: 0.8781 - val_loss: 0.3304 - val_accuracy: 0.8250\n",
            "Epoch 14/100\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 0.3287 - accuracy: 0.8844 - val_loss: 0.3238 - val_accuracy: 0.8250\n",
            "Epoch 15/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.3194 - accuracy: 0.8875 - val_loss: 0.3185 - val_accuracy: 0.8250\n",
            "Epoch 16/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.3117 - accuracy: 0.8844 - val_loss: 0.3150 - val_accuracy: 0.8250\n",
            "Epoch 17/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.3049 - accuracy: 0.8906 - val_loss: 0.3127 - val_accuracy: 0.8250\n",
            "Epoch 18/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2989 - accuracy: 0.8906 - val_loss: 0.3071 - val_accuracy: 0.8250\n",
            "Epoch 19/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 0.2930 - accuracy: 0.8938 - val_loss: 0.3041 - val_accuracy: 0.8250\n",
            "Epoch 20/100\n",
            "10/10 [==============================] - 0s 11ms/step - loss: 0.2888 - accuracy: 0.8938 - val_loss: 0.3003 - val_accuracy: 0.8375\n",
            "Epoch 21/100\n",
            "10/10 [==============================] - 0s 11ms/step - loss: 0.2844 - accuracy: 0.8969 - val_loss: 0.3004 - val_accuracy: 0.8375\n",
            "Epoch 22/100\n",
            "10/10 [==============================] - 0s 11ms/step - loss: 0.2805 - accuracy: 0.9000 - val_loss: 0.2977 - val_accuracy: 0.8375\n",
            "Epoch 23/100\n",
            "10/10 [==============================] - 0s 11ms/step - loss: 0.2775 - accuracy: 0.9000 - val_loss: 0.2950 - val_accuracy: 0.8500\n",
            "Epoch 24/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 0.2742 - accuracy: 0.9000 - val_loss: 0.2935 - val_accuracy: 0.8500\n",
            "Epoch 25/100\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 0.2717 - accuracy: 0.8938 - val_loss: 0.2930 - val_accuracy: 0.8625\n",
            "Epoch 26/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 0.2692 - accuracy: 0.8969 - val_loss: 0.2889 - val_accuracy: 0.8625\n",
            "Epoch 27/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 0.2671 - accuracy: 0.8969 - val_loss: 0.2867 - val_accuracy: 0.8625\n",
            "Epoch 28/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2645 - accuracy: 0.8969 - val_loss: 0.2858 - val_accuracy: 0.8625\n",
            "Epoch 29/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 0.2630 - accuracy: 0.9000 - val_loss: 0.2850 - val_accuracy: 0.8625\n",
            "Epoch 30/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2613 - accuracy: 0.9000 - val_loss: 0.2832 - val_accuracy: 0.8625\n",
            "Epoch 31/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2592 - accuracy: 0.9000 - val_loss: 0.2838 - val_accuracy: 0.8625\n",
            "Epoch 32/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 0.2582 - accuracy: 0.9000 - val_loss: 0.2842 - val_accuracy: 0.8625\n",
            "Epoch 33/100\n",
            "10/10 [==============================] - 0s 13ms/step - loss: 0.2564 - accuracy: 0.9031 - val_loss: 0.2803 - val_accuracy: 0.8625\n",
            "Epoch 34/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 0.2552 - accuracy: 0.9000 - val_loss: 0.2801 - val_accuracy: 0.8750\n",
            "Epoch 35/100\n",
            "10/10 [==============================] - 0s 11ms/step - loss: 0.2537 - accuracy: 0.9000 - val_loss: 0.2799 - val_accuracy: 0.8750\n",
            "Epoch 36/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 0.2526 - accuracy: 0.9000 - val_loss: 0.2783 - val_accuracy: 0.8750\n",
            "Epoch 37/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 0.2516 - accuracy: 0.9000 - val_loss: 0.2761 - val_accuracy: 0.8750\n",
            "Epoch 38/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 0.2507 - accuracy: 0.9000 - val_loss: 0.2746 - val_accuracy: 0.8750\n",
            "Epoch 39/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 0.2494 - accuracy: 0.9000 - val_loss: 0.2742 - val_accuracy: 0.8750\n",
            "Epoch 40/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 0.2485 - accuracy: 0.9000 - val_loss: 0.2745 - val_accuracy: 0.8750\n",
            "Epoch 41/100\n",
            "10/10 [==============================] - 0s 12ms/step - loss: 0.2477 - accuracy: 0.9000 - val_loss: 0.2750 - val_accuracy: 0.8750\n",
            "Epoch 42/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 0.2471 - accuracy: 0.9000 - val_loss: 0.2728 - val_accuracy: 0.8750\n",
            "Epoch 43/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2458 - accuracy: 0.9000 - val_loss: 0.2724 - val_accuracy: 0.8750\n",
            "Epoch 44/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2452 - accuracy: 0.9000 - val_loss: 0.2720 - val_accuracy: 0.8750\n",
            "Epoch 45/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 0.2444 - accuracy: 0.9000 - val_loss: 0.2724 - val_accuracy: 0.8750\n",
            "Epoch 46/100\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 0.2438 - accuracy: 0.9000 - val_loss: 0.2731 - val_accuracy: 0.8750\n",
            "Epoch 47/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2432 - accuracy: 0.9000 - val_loss: 0.2725 - val_accuracy: 0.8750\n",
            "Epoch 48/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2426 - accuracy: 0.9000 - val_loss: 0.2710 - val_accuracy: 0.8750\n",
            "Epoch 49/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2423 - accuracy: 0.9000 - val_loss: 0.2725 - val_accuracy: 0.8750\n",
            "Epoch 50/100\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 0.2414 - accuracy: 0.9000 - val_loss: 0.2710 - val_accuracy: 0.8750\n",
            "Epoch 51/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2410 - accuracy: 0.9000 - val_loss: 0.2693 - val_accuracy: 0.8750\n",
            "Epoch 52/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2407 - accuracy: 0.9000 - val_loss: 0.2694 - val_accuracy: 0.8750\n",
            "Epoch 53/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2396 - accuracy: 0.9000 - val_loss: 0.2704 - val_accuracy: 0.8750\n",
            "Epoch 54/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2394 - accuracy: 0.9000 - val_loss: 0.2699 - val_accuracy: 0.8750\n",
            "Epoch 55/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2390 - accuracy: 0.9000 - val_loss: 0.2677 - val_accuracy: 0.8750\n",
            "Epoch 56/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2385 - accuracy: 0.9000 - val_loss: 0.2694 - val_accuracy: 0.8750\n",
            "Epoch 57/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2380 - accuracy: 0.9000 - val_loss: 0.2685 - val_accuracy: 0.8750\n",
            "Epoch 58/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2374 - accuracy: 0.9000 - val_loss: 0.2679 - val_accuracy: 0.8875\n",
            "Epoch 59/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2373 - accuracy: 0.9000 - val_loss: 0.2693 - val_accuracy: 0.8750\n",
            "Epoch 60/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2369 - accuracy: 0.9000 - val_loss: 0.2681 - val_accuracy: 0.8750\n",
            "Epoch 61/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2363 - accuracy: 0.9000 - val_loss: 0.2687 - val_accuracy: 0.8875\n",
            "Epoch 62/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2366 - accuracy: 0.9031 - val_loss: 0.2705 - val_accuracy: 0.8750\n",
            "Epoch 63/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2361 - accuracy: 0.9031 - val_loss: 0.2662 - val_accuracy: 0.9125\n",
            "Epoch 64/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2353 - accuracy: 0.9094 - val_loss: 0.2679 - val_accuracy: 0.9000\n",
            "Epoch 65/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2350 - accuracy: 0.9062 - val_loss: 0.2679 - val_accuracy: 0.9000\n",
            "Epoch 66/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2347 - accuracy: 0.9094 - val_loss: 0.2683 - val_accuracy: 0.9000\n",
            "Epoch 67/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2346 - accuracy: 0.9062 - val_loss: 0.2688 - val_accuracy: 0.8875\n",
            "Epoch 68/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2339 - accuracy: 0.9094 - val_loss: 0.2671 - val_accuracy: 0.9125\n",
            "Epoch 69/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2339 - accuracy: 0.9062 - val_loss: 0.2673 - val_accuracy: 0.9125\n",
            "Epoch 70/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2335 - accuracy: 0.9094 - val_loss: 0.2662 - val_accuracy: 0.9125\n",
            "Epoch 71/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2333 - accuracy: 0.9094 - val_loss: 0.2657 - val_accuracy: 0.9125\n",
            "Epoch 72/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2330 - accuracy: 0.9094 - val_loss: 0.2670 - val_accuracy: 0.9125\n",
            "Epoch 73/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2329 - accuracy: 0.9094 - val_loss: 0.2650 - val_accuracy: 0.9125\n",
            "Epoch 74/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2329 - accuracy: 0.9094 - val_loss: 0.2682 - val_accuracy: 0.9125\n",
            "Epoch 75/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2323 - accuracy: 0.9094 - val_loss: 0.2659 - val_accuracy: 0.9125\n",
            "Epoch 76/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2322 - accuracy: 0.9094 - val_loss: 0.2665 - val_accuracy: 0.9125\n",
            "Epoch 77/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2321 - accuracy: 0.9062 - val_loss: 0.2656 - val_accuracy: 0.9125\n",
            "Epoch 78/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2315 - accuracy: 0.9094 - val_loss: 0.2654 - val_accuracy: 0.9125\n",
            "Epoch 79/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2317 - accuracy: 0.9094 - val_loss: 0.2669 - val_accuracy: 0.9250\n",
            "Epoch 80/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2310 - accuracy: 0.9094 - val_loss: 0.2663 - val_accuracy: 0.9125\n",
            "Epoch 81/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2311 - accuracy: 0.9094 - val_loss: 0.2665 - val_accuracy: 0.9125\n",
            "Epoch 82/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 0.2305 - accuracy: 0.9062 - val_loss: 0.2651 - val_accuracy: 0.9125\n",
            "Epoch 83/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2308 - accuracy: 0.9094 - val_loss: 0.2633 - val_accuracy: 0.9125\n",
            "Epoch 84/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2306 - accuracy: 0.9094 - val_loss: 0.2649 - val_accuracy: 0.9250\n",
            "Epoch 85/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2300 - accuracy: 0.9094 - val_loss: 0.2649 - val_accuracy: 0.9125\n",
            "Epoch 86/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2299 - accuracy: 0.9062 - val_loss: 0.2645 - val_accuracy: 0.9125\n",
            "Epoch 87/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2298 - accuracy: 0.9062 - val_loss: 0.2666 - val_accuracy: 0.9250\n",
            "Epoch 88/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2297 - accuracy: 0.9062 - val_loss: 0.2654 - val_accuracy: 0.9250\n",
            "Epoch 89/100\n",
            "10/10 [==============================] - 0s 6ms/step - loss: 0.2295 - accuracy: 0.9094 - val_loss: 0.2624 - val_accuracy: 0.9125\n",
            "Epoch 90/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2292 - accuracy: 0.9094 - val_loss: 0.2642 - val_accuracy: 0.9125\n",
            "Epoch 91/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2292 - accuracy: 0.9125 - val_loss: 0.2676 - val_accuracy: 0.9250\n",
            "Epoch 92/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2289 - accuracy: 0.9125 - val_loss: 0.2673 - val_accuracy: 0.9250\n",
            "Epoch 93/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2288 - accuracy: 0.9125 - val_loss: 0.2650 - val_accuracy: 0.9250\n",
            "Epoch 94/100\n",
            "10/10 [==============================] - 0s 7ms/step - loss: 0.2286 - accuracy: 0.9094 - val_loss: 0.2653 - val_accuracy: 0.9250\n",
            "Epoch 95/100\n",
            "10/10 [==============================] - 0s 10ms/step - loss: 0.2288 - accuracy: 0.9125 - val_loss: 0.2630 - val_accuracy: 0.9125\n",
            "Epoch 96/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2289 - accuracy: 0.9125 - val_loss: 0.2642 - val_accuracy: 0.9125\n",
            "Epoch 97/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2282 - accuracy: 0.9125 - val_loss: 0.2622 - val_accuracy: 0.9125\n",
            "Epoch 98/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2284 - accuracy: 0.9125 - val_loss: 0.2629 - val_accuracy: 0.9125\n",
            "Epoch 99/100\n",
            "10/10 [==============================] - 0s 8ms/step - loss: 0.2281 - accuracy: 0.9125 - val_loss: 0.2660 - val_accuracy: 0.9250\n",
            "Epoch 100/100\n",
            "10/10 [==============================] - 0s 9ms/step - loss: 0.2276 - accuracy: 0.9125 - val_loss: 0.2642 - val_accuracy: 0.9125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(history.history['val_accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "InsXWldoAVWW",
        "outputId": "bbb3ccea-02f2-4020-ccdd-724e577dd080"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x788b3be9be20>]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o_NJJT__Abp1"
      },
      "execution_count": 24,
      "outputs": []
    }
  ]
}