Handling_Overfitting_dropout_regularization_technique-Sonar Dataset.ipynb

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      "source": [
        "**<h2 align='center' style='color:blue'> Handling Overfitting In Deep Neural Network </h2>**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKlep7YnnVCK"
      },
      "source": [
        "This is a dataset that describes sonar chirp returns bouncing off different services. The 60 input variables are the strength of the returns at different angles. It is a **binary classification problem** that requires a model to differentiate rocks from metal cylinders.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HX1fKR9InVCO"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
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              "         0       1       2       3       4       5       6       7       8   \\\n",
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              "70   0.0065  0.0122  0.0068  0.0108  0.0217  0.0284  0.0527  0.0575  0.1054   \n",
              "\n",
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              "70   0.1109  ...  0.0069  0.0025  0.0027  0.0052  0.0036  0.0026  0.0036   \n",
              "\n",
              "         58      59    60  \n",
              "178  0.0047  0.0054  Mine  \n",
              "24   0.0095  0.0011  Rock  \n",
              "143  0.0074  0.0078  Mine  \n",
              "111  0.0134  0.0056  Mine  \n",
              "70   0.0006  0.0035  Rock  \n",
              "\n",
              "[5 rows x 61 columns]"
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          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "df = pd.read_csv(\"/content/sonar.csv\", header=None, skiprows = 1)\n",
        "df.sample(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gxhnR8zInVCW",
        "outputId": "112a2653-3b5a-4f74-a704-edfe3c621f4b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Rock\n",
              "1    Rock\n",
              "2    Rock\n",
              "3    Rock\n",
              "4    Rock\n",
              "Name: 60, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "X = df.drop(60, axis=1)\n",
        "y = df[60]\n",
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "i35x02VCv-1a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BdM62mvcnVCX",
        "outputId": "b7b64598-e868-4614-bd72-a86295b05138",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoder = LabelEncoder()\n",
        "\n",
        "\n",
        "y = encoder.fit_transform(y)\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "67GjV0-DnVCa"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)\n",
        "\n",
        "_, x_val,_, y_val = train_test_split(X_test,y_test, test_size=0.25, random_state=1) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9KJTw029nVCb"
      },
      "source": [
        "# Using Deep Learning Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hgIh_aiqnVCb"
      },
      "source": [
        "## Building Our NN Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wr_cjyJsnVCc"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "lf3t4dy3nVCc",
        "outputId": "afc5cb15-02ba-4657-8ba6-9c141bbbeb10",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "14/14 [==============================] - 2s 38ms/step - loss: 0.7261 - accuracy: 0.3945 - val_loss: 0.6879 - val_accuracy: 0.4681\n",
            "Epoch 2/100\n",
            "14/14 [==============================] - 0s 10ms/step - loss: 0.6790 - accuracy: 0.6330 - val_loss: 0.6757 - val_accuracy: 0.5319\n",
            "Epoch 3/100\n",
            "14/14 [==============================] - 0s 8ms/step - loss: 0.6738 - accuracy: 0.5413 - val_loss: 0.6644 - val_accuracy: 0.5319\n",
            "Epoch 4/100\n",
            "14/14 [==============================] - 0s 9ms/step - loss: 0.6649 - accuracy: 0.5505 - val_loss: 0.6503 - val_accuracy: 0.6596\n",
            "Epoch 5/100\n",
            "14/14 [==============================] - 0s 7ms/step - loss: 0.6480 - accuracy: 0.5963 - val_loss: 0.6385 - val_accuracy: 0.7021\n",
            "Epoch 6/100\n",
            "14/14 [==============================] - 0s 11ms/step - loss: 0.6343 - accuracy: 0.6330 - val_loss: 0.6245 - val_accuracy: 0.7021\n",
            "Epoch 7/100\n",
            "14/14 [==============================] - 0s 15ms/step - loss: 0.6174 - accuracy: 0.6697 - val_loss: 0.6111 - val_accuracy: 0.7021\n",
            "Epoch 8/100\n",
            "14/14 [==============================] - 0s 11ms/step - loss: 0.5960 - accuracy: 0.7339 - val_loss: 0.5936 - val_accuracy: 0.7447\n",
            "Epoch 9/100\n",
            "14/14 [==============================] - 0s 17ms/step - loss: 0.5816 - accuracy: 0.7615 - val_loss: 0.5736 - val_accuracy: 0.7447\n",
            "Epoch 10/100\n",
            "14/14 [==============================] - 0s 18ms/step - loss: 0.5571 - accuracy: 0.7248 - val_loss: 0.5538 - val_accuracy: 0.7660\n",
            "Epoch 11/100\n",
            "14/14 [==============================] - 0s 15ms/step - loss: 0.5373 - accuracy: 0.7982 - val_loss: 0.5395 - val_accuracy: 0.7660\n",
            "Epoch 12/100\n",
            "14/14 [==============================] - 0s 12ms/step - loss: 0.5093 - accuracy: 0.7523 - val_loss: 0.5134 - val_accuracy: 0.8085\n",
            "Epoch 13/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5034 - accuracy: 0.7615 - val_loss: 0.4970 - val_accuracy: 0.7660\n",
            "Epoch 14/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.4773 - accuracy: 0.7431 - val_loss: 0.4832 - val_accuracy: 0.8085\n",
            "Epoch 15/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.4503 - accuracy: 0.8257 - val_loss: 0.5232 - val_accuracy: 0.7660\n",
            "Epoch 16/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.4338 - accuracy: 0.7890 - val_loss: 0.4718 - val_accuracy: 0.8085\n",
            "Epoch 17/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.3996 - accuracy: 0.8349 - val_loss: 0.5035 - val_accuracy: 0.8085\n",
            "Epoch 18/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.4038 - accuracy: 0.8165 - val_loss: 0.4885 - val_accuracy: 0.8298\n",
            "Epoch 19/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3736 - accuracy: 0.8624 - val_loss: 0.4894 - val_accuracy: 0.8085\n",
            "Epoch 20/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3629 - accuracy: 0.8440 - val_loss: 0.4849 - val_accuracy: 0.8298\n",
            "Epoch 21/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3630 - accuracy: 0.8624 - val_loss: 0.4624 - val_accuracy: 0.8511\n",
            "Epoch 22/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3547 - accuracy: 0.8257 - val_loss: 0.5041 - val_accuracy: 0.8298\n",
            "Epoch 23/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.3206 - accuracy: 0.8899 - val_loss: 0.5222 - val_accuracy: 0.8298\n",
            "Epoch 24/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3076 - accuracy: 0.8899 - val_loss: 0.4871 - val_accuracy: 0.8298\n",
            "Epoch 25/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2939 - accuracy: 0.9174 - val_loss: 0.5210 - val_accuracy: 0.8298\n",
            "Epoch 26/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2864 - accuracy: 0.8899 - val_loss: 0.5421 - val_accuracy: 0.8085\n",
            "Epoch 27/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2755 - accuracy: 0.8899 - val_loss: 0.5086 - val_accuracy: 0.8298\n",
            "Epoch 28/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2665 - accuracy: 0.9266 - val_loss: 0.5523 - val_accuracy: 0.8085\n",
            "Epoch 29/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.2466 - accuracy: 0.9541 - val_loss: 0.5415 - val_accuracy: 0.7872\n",
            "Epoch 30/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2564 - accuracy: 0.9083 - val_loss: 0.5408 - val_accuracy: 0.8298\n",
            "Epoch 31/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2645 - accuracy: 0.8716 - val_loss: 0.6244 - val_accuracy: 0.8085\n",
            "Epoch 32/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2488 - accuracy: 0.8991 - val_loss: 0.6298 - val_accuracy: 0.8085\n",
            "Epoch 33/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2466 - accuracy: 0.8991 - val_loss: 0.5732 - val_accuracy: 0.8085\n",
            "Epoch 34/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2107 - accuracy: 0.9266 - val_loss: 0.5439 - val_accuracy: 0.8298\n",
            "Epoch 35/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2162 - accuracy: 0.9266 - val_loss: 0.5492 - val_accuracy: 0.8298\n",
            "Epoch 36/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.2796 - accuracy: 0.8440 - val_loss: 0.6007 - val_accuracy: 0.7872\n",
            "Epoch 37/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.2233 - accuracy: 0.9174 - val_loss: 0.6831 - val_accuracy: 0.7872\n",
            "Epoch 38/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.1916 - accuracy: 0.9266 - val_loss: 0.5827 - val_accuracy: 0.8511\n",
            "Epoch 39/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1595 - accuracy: 0.9908 - val_loss: 0.6479 - val_accuracy: 0.8085\n",
            "Epoch 40/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1700 - accuracy: 0.9633 - val_loss: 0.6142 - val_accuracy: 0.7872\n",
            "Epoch 41/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1533 - accuracy: 0.9450 - val_loss: 0.5982 - val_accuracy: 0.8298\n",
            "Epoch 42/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.1685 - accuracy: 0.9450 - val_loss: 0.6309 - val_accuracy: 0.7872\n",
            "Epoch 43/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.1347 - accuracy: 0.9908 - val_loss: 0.6450 - val_accuracy: 0.8298\n",
            "Epoch 44/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1381 - accuracy: 0.9817 - val_loss: 0.7288 - val_accuracy: 0.8085\n",
            "Epoch 45/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1412 - accuracy: 0.9725 - val_loss: 0.6516 - val_accuracy: 0.8085\n",
            "Epoch 46/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1251 - accuracy: 0.9817 - val_loss: 0.6741 - val_accuracy: 0.8298\n",
            "Epoch 47/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1139 - accuracy: 0.9908 - val_loss: 0.6799 - val_accuracy: 0.8085\n",
            "Epoch 48/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1104 - accuracy: 0.9817 - val_loss: 0.6959 - val_accuracy: 0.7872\n",
            "Epoch 49/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1027 - accuracy: 0.9908 - val_loss: 0.7580 - val_accuracy: 0.8298\n",
            "Epoch 50/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.1053 - accuracy: 0.9908 - val_loss: 0.7423 - val_accuracy: 0.8085\n",
            "Epoch 51/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0899 - accuracy: 0.9908 - val_loss: 0.7281 - val_accuracy: 0.8085\n",
            "Epoch 52/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0831 - accuracy: 0.9908 - val_loss: 0.7644 - val_accuracy: 0.7872\n",
            "Epoch 53/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0792 - accuracy: 0.9908 - val_loss: 0.7847 - val_accuracy: 0.8085\n",
            "Epoch 54/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0714 - accuracy: 1.0000 - val_loss: 0.8115 - val_accuracy: 0.8085\n",
            "Epoch 55/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0717 - accuracy: 0.9908 - val_loss: 0.7933 - val_accuracy: 0.8085\n",
            "Epoch 56/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0658 - accuracy: 1.0000 - val_loss: 0.8479 - val_accuracy: 0.7872\n",
            "Epoch 57/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0629 - accuracy: 1.0000 - val_loss: 0.8212 - val_accuracy: 0.7872\n",
            "Epoch 58/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0549 - accuracy: 1.0000 - val_loss: 0.8590 - val_accuracy: 0.8085\n",
            "Epoch 59/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0504 - accuracy: 1.0000 - val_loss: 0.8778 - val_accuracy: 0.8085\n",
            "Epoch 60/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0530 - accuracy: 1.0000 - val_loss: 0.8341 - val_accuracy: 0.8085\n",
            "Epoch 61/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0468 - accuracy: 1.0000 - val_loss: 0.8566 - val_accuracy: 0.8085\n",
            "Epoch 62/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0445 - accuracy: 1.0000 - val_loss: 0.8706 - val_accuracy: 0.8085\n",
            "Epoch 63/100\n",
            "14/14 [==============================] - 0s 7ms/step - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.8924 - val_accuracy: 0.8298\n",
            "Epoch 64/100\n",
            "14/14 [==============================] - 0s 7ms/step - loss: 0.0325 - accuracy: 1.0000 - val_loss: 0.9175 - val_accuracy: 0.8085\n",
            "Epoch 65/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0344 - accuracy: 1.0000 - val_loss: 0.8771 - val_accuracy: 0.8085\n",
            "Epoch 66/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0306 - accuracy: 1.0000 - val_loss: 0.9062 - val_accuracy: 0.8085\n",
            "Epoch 67/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0244 - accuracy: 1.0000 - val_loss: 0.9204 - val_accuracy: 0.8085\n",
            "Epoch 68/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0218 - accuracy: 1.0000 - val_loss: 0.9425 - val_accuracy: 0.8085\n",
            "Epoch 69/100\n",
            "14/14 [==============================] - 0s 7ms/step - loss: 0.0198 - accuracy: 1.0000 - val_loss: 0.9538 - val_accuracy: 0.8085\n",
            "Epoch 70/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0193 - accuracy: 1.0000 - val_loss: 1.0061 - val_accuracy: 0.8085\n",
            "Epoch 71/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0252 - accuracy: 1.0000 - val_loss: 0.9987 - val_accuracy: 0.8298\n",
            "Epoch 72/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0182 - accuracy: 1.0000 - val_loss: 1.0073 - val_accuracy: 0.8085\n",
            "Epoch 73/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0202 - accuracy: 1.0000 - val_loss: 0.9726 - val_accuracy: 0.8298\n",
            "Epoch 74/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0143 - accuracy: 1.0000 - val_loss: 1.0178 - val_accuracy: 0.8085\n",
            "Epoch 75/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0124 - accuracy: 1.0000 - val_loss: 1.0235 - val_accuracy: 0.8085\n",
            "Epoch 76/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0130 - accuracy: 1.0000 - val_loss: 1.0133 - val_accuracy: 0.8298\n",
            "Epoch 77/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0114 - accuracy: 1.0000 - val_loss: 1.0763 - val_accuracy: 0.8085\n",
            "Epoch 78/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0110 - accuracy: 1.0000 - val_loss: 1.0199 - val_accuracy: 0.8298\n",
            "Epoch 79/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 1.0533 - val_accuracy: 0.8085\n",
            "Epoch 80/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 1.0734 - val_accuracy: 0.8298\n",
            "Epoch 81/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0094 - accuracy: 1.0000 - val_loss: 1.0597 - val_accuracy: 0.8298\n",
            "Epoch 82/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 1.1041 - val_accuracy: 0.8298\n",
            "Epoch 83/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0076 - accuracy: 1.0000 - val_loss: 1.0708 - val_accuracy: 0.8298\n",
            "Epoch 84/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0104 - accuracy: 1.0000 - val_loss: 1.0910 - val_accuracy: 0.8085\n",
            "Epoch 85/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 1.1135 - val_accuracy: 0.8298\n",
            "Epoch 86/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0067 - accuracy: 1.0000 - val_loss: 1.1035 - val_accuracy: 0.8085\n",
            "Epoch 87/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0057 - accuracy: 1.0000 - val_loss: 1.1161 - val_accuracy: 0.8085\n",
            "Epoch 88/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0052 - accuracy: 1.0000 - val_loss: 1.1103 - val_accuracy: 0.8298\n",
            "Epoch 89/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0052 - accuracy: 1.0000 - val_loss: 1.1331 - val_accuracy: 0.8298\n",
            "Epoch 90/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.1421 - val_accuracy: 0.8085\n",
            "Epoch 91/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 1.1411 - val_accuracy: 0.8085\n",
            "Epoch 92/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 1.1537 - val_accuracy: 0.8085\n",
            "Epoch 93/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 1.1578 - val_accuracy: 0.8085\n",
            "Epoch 94/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 1.1567 - val_accuracy: 0.8298\n",
            "Epoch 95/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 1.1771 - val_accuracy: 0.8085\n",
            "Epoch 96/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 1.1735 - val_accuracy: 0.8085\n",
            "Epoch 97/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 1.1849 - val_accuracy: 0.8298\n",
            "Epoch 98/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 1.1801 - val_accuracy: 0.8085\n",
            "Epoch 99/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 1.1962 - val_accuracy: 0.8085\n",
            "Epoch 100/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 1.2039 - val_accuracy: 0.8298\n"
          ]
        }
      ],
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(80, input_dim=60, activation='relu'),\n",
        "    keras.layers.Dense(30, activation='relu'),\n",
        "    keras.layers.Dense(15, activation='relu'),\n",
        "    keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "H = model.fit(X_train, y_train, epochs=100, batch_size=8, validation_split=0.3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zswuJAIpnVCd",
        "outputId": "1df158c2-075d-4c82-99ac-808b344f0d0d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 5ms/step - loss: 1.1332 - accuracy: 0.7692\n",
            "Test Accuracy:  76.92307829856873\n"
          ]
        }
      ],
      "source": [
        "loss, acc = model.evaluate(X_test, y_test)\n",
        "print('Test Accuracy: ', acc*100)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, acc = model.evaluate(X_train, y_train)\n",
        "print('Training Accuracy: ', acc*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OMU_Yy7XxLvX",
        "outputId": "b3a5c91e-3571-4fee-a684-b269061f96a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 0s 3ms/step - loss: 0.3648 - accuracy: 0.9487\n",
            "Training Accuracy:  94.87179517745972\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our model is overfitting"
      ],
      "metadata": {
        "id": "HEPephzDedkn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "epochs = np.arange(0, len(H.history[\"loss\"]))\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.style.use(\"ggplot\")\n",
        "\n",
        "\n",
        "# plot training and validation loss\n",
        "plt.style.use(\"ggplot\")\n",
        "plt.plot(epochs, H.history[\"loss\"], label=\"train_loss\")\n",
        "plt.plot(epochs, H.history[\"val_loss\"], label=\"val_loss\")\n",
        "plt.title(\"Training Vs Validation Loss\")\n",
        "plt.xlabel(\"Epoch #\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "m_5dGopOxspf",
        "outputId": "cb8e2759-4a05-4268-bc5b-94a6124f516b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5e7f6523d0>"
            ]
          },
          "metadata": {},
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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R5DsOFjabLWhr6+30bIKTnktw0nPpOn7T5LXVOfx17SGqPT4mZ8Vx/YQMBic134vXlc8maALZ/v37ef7555k9ezaRkZEtHvftubSAJn27brcbq9X63dNOSG8eQ+Z2u4N2bIPGXQQvPZvgpOcSnPRcuoZpmvzx6wI+2l3OqekR/OjkBPrHOgEXxcWuZs/pyjFkQRHIiouLefzxx/mf//mfVosVERERaS/TNHlxbSEf7S7nByPiueqUBAzDCHRZjXRJIHvqqafYunUrVVVV3Hzzzfzwhz9saHE699xz+cc//kF1dTUvvvgicHgy1Pnz53dFaSIiItKDmabJaxuKeH9HGRcNjQ3KMAZdNO1FZ9K0F51L017I8dCzCU56LsFJz+XErcut5v0dZWTFOZmQHkFWnBPLkdD112+K+NumEs4bFMPN45PbFcZ6XZdlbzZo0CB27drV7L6cnByuvfZali1b1sVViYiIBL96n8nrG4t4e1sp0U4r6/Nq+L/NJcSF2piQHoHdavDu9jLOHhDNTe0MY11NgUxERESCktvr51Clh4wYBzZL4zCVW+nh8S9y2VPqYsagGK4fk4TbZ7LmUDVfH6zms+wKXF6TyZlR/M+pKQ0tZsGqRweyF9cUkF3W/JsT7WEYBkd7dvvHOrlxXHKLxz700EOkpqZy3XXXAbBgwQKsViurVq2ioqICr9fLXXfdxfTp09tVg8vlYvbs2XzzzTdYrVbmzp3LpEmT2LFjB3feeScejwfTNPnzn/9MSkoKN910E3l5efj9fm6//XYuvvji4/78IiIiXc3nN3no80NsyKvBbjUYHO9kWGIYwxJDKXN5eWFNITYLzJ6cxml9D8/O4LDB2QOiOXtANB6fn+wyNwPjnFgtwR3GoIcHskCYOXMmc+fObQhk7733Hm+88QazZs0iMjKS0tJSLrroIs4999x2NZ3+5S9/wTAMPvnkE3bv3s2PfvQjVqxYwcKFC5k1axaXXnopHo8Hn8/HsmXLSElJYeHChQBUVlZ2xkcVERHpNP/YUsKGvBouGRaH12+yraiOt7aW4D8y8n1EUih3TEwlMTyk2fPtVgtDEkK7sOIT06MDWWstWe3RnkH9I0eOpLi4mPz8fEpKSoiOjiYpKYn77ruPr776CsMwyM/Pp6ioiKSkpDbXsHr1aq6//noABg4cSHp6Onv37mXs2LE888wz5OXlMWPGDAYMGMDQoUO5//77efDBB5k2bRqnnnrqcX1uERGRQNiYX8NfvylmSv8orh2d2NCAUVfvZ1dJHVUeH6elR3aLlq+26pK1LHubCy+8kA8++IB3332XmTNnsmjRIkpKSli8eDFLliwhISEBt9vdIfe65JJLeOWVV3A6nVx99dWsXLmSrKwsPvzwQ4YOHcqjjz7Kk08+2SH3EhER6WwltfUs+CKX9Gg7P52Q0qg3KTTEwskp4UzKiOpRYQwUyDrFzJkzeeedd/jggw+48MILqaqqIiEhgZCQEL744gsOHjzY7mtOmDCBf/7znwDs2bOHQ4cOkZWVxf79+8nMzGTWrFlMnz6dbdu2kZ+fT2hoKJdddhk333wzmzZt6uiPKCIi0uF8fpPHV+biqvdz95lpOG29J6b06C7LQBkyZAg1NTWkpKSQnJzMpZdeyrXXXsvUqVM5+eSTGThwYLuvee211zJ79mymTp2K1WrlySefxOFw8N577/HWW29hs9lISkri1ltvZePGjTzwwAMYhkFISAgPP/xwJ3xKERGRjvX6xiK2FtVxx8Q+9I12BLqcLqWJYdtAE8NqYlhpHz2b4KTnEpx6y3Px+PxsL6pjY34tG/NrqPH4iHRYibBbiXRYCbEYLNlTwfSBMdxyakqgywU0MayIiIj0AKZpsmRPBV/sr2RrUR0en4nVgCEJoQyIc1Lt9lHu8pJT4aHK7ePk5DBuHNf2F956EgWyILBt2zZuu+22RtscDgfvv/9+gCoSERE5MS6vn9//O4+V+6tIj7IzfVAMo1LCGZ4USliINdDlBR0FsiAwbNgwlixZEugyREREOkRBtYeHPz/EvjI314xK5NLhcUG9bFEw6HGBrJsPiQs6+j5FRKQ9NhXU8OiKXHx+k3umpDMuLSLQJXULPS6QWSwWvF4vNluP+2hdzuv1YrH0nleORUSkbXaV1PHOtlL8JkTYrYTbLUTYrdTW+1m0tYTUSDtzzkonLcoe6FK7jR6XWpxOJy6XC7fb3WHNow6Ho8Mmcu0uTNPEYrHgdDoDXYqIiASJsjovCzcU8cneCiLtFiIdNmrqfdR4fHj9h48ZnxbBnZP6aJxYO/W4QGYYBqGhHbt2VW95JVlERKQ59T4/720v4++bS/D6/Vw6PI7LR8Y3hC7TNPH4TOq8fqIdVo0XOw49LpCJiIhIx8mr8vC7T3PIq6pnfFoEN4xJIvU7XZGGYeCwGTh60cz6HU2BTERERJrl85s8tSqPSpePud9LZ0yqBuh3FkVZERERadY720rZXlzHT8YnK4x1MgUyERERaWJ/uZs3vinm9L4RnNUvKtDl9HgKZCIiItJIvc/kqVW5hIdY+OmEFA3S7wIKZCIiIr2QaZrU1vua3ffmlmL2lrm55dQUop0abt4V9C2LiIj0IEU19QAkhoe0eExZnZdHVhxie1EdJ6WEMXVANKf3jcRhs7CrpI43N5fwvf5RnNY3sqvK7vUUyERERHqISpeXX324j2qPn/86KYHvD4/DZmnc3birpI6Hlx+iyuNjxuAY1ubW8OSqPP5kK2BSZiTbi+qIDbVx47jkAH2K3kmBTEREpAcwTZPnvi6gyuNnVEoYCzcWsfJAJbee1oesuMOrrnyWXcGzX+UT47TyyLmZDIhz4jdNthbW8cneClbur8TlNbnv7L5E2DXTfldSIBMREQkypXVeYp3tm/H+s+xKvsyp4tpRiVw6Ip4vc6p4/ut8fvnhPr4/LA6/CW9vK2VkUih3nZnWMDbMYhiMTA5jZHIYPxmXTFFtPRnRjs76aNICBTIREZEgcqDCze0fZPPTCSmcOzCmTecU1dTz5zUFDE8M5eJhcQCc3jeSk5LCeGV9IYu2lgJw/uAYZo1NbtKNeVRoiEVhLEAUyERERILI8uxK/Ca8taWEqQOisbYQno7ymybPfJmH34TbT+/T6PgIh5VbT+vD2f2jqfT4OF2D9IOWpr0QEREJEqZp8vm+SqIdVvKr6/niQNUxz/lgRxnfFNQya2wSKZH2Zo8ZkRymMBbkFMhERESCxI5iF4U19Vw7OpH0KDuLtpZgmmaLx+dUuHltQxHj08I5Jyu6CyuVjqZAJiIiEiQ+31eB3WpwekYklwyPI7vMzfq8mmaPrff5eXJVLg6bhZ+d2kez6XdzCmQiIiJBwOc3WXmginFpEYSFWDmrXzTxoTbeOjIg/7teXFvInlI3t56WQmyohoR3dwpkIiIiQeCbgloqXD4mH1nIO8RqcPGwODYX1LKjuK7Rscv2VvDhrnIuHR7HqekaG9YTKJCJiIgEgc/3VRAeYmFsanjDtnMHxhBht/DWlpKGbfvKXPzx63xGJoVy1SmJgShVOoECmYiISIC5vX6+PFDNaX0jsVv/809zaIiFC4bE8tXBanIq3NR4fDyy4hDhdiu/PCPtmFNiSPehTmcREZE2emTFIUIsBndOSj2u871+s9lJWdfmVlPn9Td0V37bhYNj+efWUhZtLaGu3k9+dT0PTMvQuLEeRk9TRESkDep9ftYcqm74c4i1fZ1Mb28r4X83FnPb6X04I7Nx8Pp8XyUxTisnJYc1OS/KaePcgTG8v6MMgOvHJDIiqelx0r2py1JERKQNdpa48PhMPD6TrUV1xz7hW77Jr+HV9UVYDIPHVubyzrbShvnFqtxe1hyq4czMqBa7IC8eGnd4Ooy+kVw8NO6EP4sEHwUyERGRNthSUIsBWA3Y0MLcYM0pqa3n8ZW5pEba+fPFA5iYEcnL6wp5aW0hPr/J57tLqPebnNlMd+VRSREh/GnmAH51RqrmG+uh1GUpIiLSBpsLa8mMcRDhsLI+r4ZrRx/7nHqfySMrcnH7TB6cnEaU08avzkjllXWFvLu9jOLaelx+CykRIQyOd7Z6rfiwkA76JBKM1EImIiJyDPU+k21FdYxMDmN0SjjZZW7K6rzHPO8v6wvZUVzHrael0DfaAYDFMJg1NplZY5P4d0416w9VMrlflFq+ejkFMhERkWPYXVqHx2cyMimM0UfmCTtWt+Xn+yp5f0cZFw2NbTKIH2Dm0Dh+dWYqgxPDmaZ1KHs9BTIREZFj2FJweBD/iKRQ+sc6iHZYWw1kORVunv0qj2GJoVw3OqnF4yZlRPHKlaNJjrB3eM3SvSiQiYiIHMPmwloyou1EOW1YDINT+oSzPr8G/5E3Jb/r1fWFhFgMfnVGarPzjol8lwKZiIhIK7z+w+PHvj331+g+4VS4fOwrczc5fn+5m9WHarhwaJwG4kubKZCJiIi0Yk+pC5fXz8hvTdo6qs/hcWTrm+m2/OfWEhxWg/MHx3ZZjdL9KZCJiIi0YktBLQAjv9VCFhdqo1+Mo0kgK6qp5/N9lZw7MIYoh7VL65TuTYFMRESkFZsLa0mPshPznbUjR/UJZ1tRLS6vv2HbO9tKAbh4mGbTl/ZRIBMREWmBr5nxY0eN7hOO1w+bj7SgVbp9fLy7nMn9okgM19gxaR8FMhERkRbsLXNRW994/NhRw5NCsVuNhm7Lf+0ow+0zuXR4fFeXKT2AApmIiEgLthQebv0akRTaZJ/damFkUhjr82pwef28v7OM8WnhZMQ4urpM6QEUyERERFqwuaCO1MiQFqevGJ0azqFKD3/9ppgqt4/L1Domx0mBTEREei2f32R9Xg3Pr87nywNVTfZtLaptdvzYUUenv3h7WynDEkMZ1sqxIq2xHfsQERGRniWnws2yvRUsz66kpM6LxYB/7SxnYkYkN41LJibUxv5yNzWe5sePHdU3yk58mI2SWi+XDteblXL8FMhERKTXqKv388Dyg2wuqMViwNjUcGYNSGJsagTv7yjjr98Us6mglh+PTaLC7QNotYXMMAym9ItiS2Ed49IiuupjSA+kQCYiIr3Gv3Oq2FxQy49OSuC8QTGN5hb7wYh4Tk2P4Pf/zuOJVXk4rAbJESHHnMLimlYWDxdpK40hExGRXmPF/kqSwm1ccVJ8k4leAfpGO3j4nExuGJOECYxLDe/6IqVX6pIWsueee45169YRHR3NggULmuw3TZNXXnmF9evX43A4uOWWWxgwYEBXlCYiIj1Abb2PnAoPQxKaTk9xVKXLy4a8Gi4eFodhGC0eZ7UYXDwsjmlZ0ditLR8n0pG6pIVsypQpzJkzp8X969evJz8/n2eeeYaf/OQnvPjii11RloiIBLl3tpVy10f7+HxfJT6/2WS/z2+yeGcZN7+zl7s+2s+2otoWr/VlTjU+E87MjGrTvcPtVkKs6kiSrtEl/0sbPnw4EREtD3Zcs2YNkydPxjAMBg8eTE1NDWVlZV1RmoiIBCnTNPlgZxm7Slws+CKXn72/l493l1Pv82OaJqsPVnPbB9n8aXUB6dF2IuyWhrUkm7NifyWpkXb6x2riVgk+QTGov7S0lISEhIaf4+PjKS0tJTY2NoBViYhIIOVUeiiorufm8YenoXhzcwnPfpXPX78pJiUihK1FdaRG2pkzOY0J6RG8vrGYt7aUkFfloU+kvdG1Suu8bC6o5YcnxbfaXSkSKEERyNpj6dKlLF26FID58+c3CnKdxWazdcl9pH30XIKXnk1w6m7P5aN9BwE47+QMEiMcXDiqH6sPlPPamoMcKq/jjikD+P7IFGxHuhWvOi2Kt7eVsnR/HXdMSW10rU835GICM0dlkhAXXJO3drfn0pt05bMJikAWFxdHcXFxw88lJSXExTU/wd60adOYNm1aw8/fPq+zJCQkdMl9pH30XIKXnk1w6m7P5bOdBQyIdWC4qih2HZ5Ff0A43HdWn4Zjysv+00VpAGdmRvL+lnwuGRRBhMPasG/xljz6xzqI8NdSXNzyOLNA6G7PpTfp6GeTmpra4r6gGK04btw4Pv/8c0zTZOfOnYSFham7UkSkF6ty+9he3P7JVi8eFofLa/LR7vKGbQXVHnYU17XyZIcAACAASURBVHFGGwfziwRCl7SQPfXUU2zdupWqqipuvvlmfvjDH+L1egE499xzGT16NOvWreO2227Dbrdzyy23dEVZIiISpNbmVuM3YXw7A1n/WCenpITx/o4yZg6NI8RqsHL/4da1MzMjO6NUkQ7RJYHs5z//eav7DcPgxhtv7IpSRESkG1hzqJpop5WB8c52n3vx0Dju/+wgK/dX8r0B0azYX8ngeCfJEfZjnywSIEHRZSkiInKU12+yLq+GcakRWI7jjcgxqeH0jbbzzvZSDla4yS5zM7mfuisluCmQiYhIUNleVEeNx8/49ONbrNswDC4eGkd2mZvnVxdgABMz1F0pwU2BTEREgsrqQ9XYLAanpBz/9BRn9Y8i2mnlm4JaRiSHER/W+gLhIoGmQCYiIkFlzaFqRiaHERZiPfbBLbBbLZw/+PDb+hrML91BUMxDJiIiApBX5eFgpYcZg2NO+Fozh8ZimiZT+kd3QGUinUuBTEREgsbqQ9VA+6e7aE5YiJUfnZx4wtcR6QrqshQRkaCx+lA1GdF2TVEhvY4CmYiIBIXaeh9bCmrbPTu/SE+gQCYiIkFhfV4NvuOYnV+kJ1AgExGRgPP6TT7dW0Gk3cKQhNBAlyPS5TSoX0REAiq/ysOCL3LZWeLiRycnYLW0f3Z+ke5OgUxERALms+wK/vR1ARYL3HVGKpMytcSR9E4KZCIi0uVq63386esClu+rZHhiKHdOSiUxXLPpS++lQCYiIl3K7fXz648OkFPp5sqTE/jBiHh1U0qvp0AmIiJd6pV1heyvcHPvlHRNcSFyhN6yFBGRLrPmUDWLd5Vz8dBYhTGRb1EgExGRLlHh8vL7f+eRGePgqlFa0kjk2xTIRESk05mmybNf5VPt8XPnxD7YrfrnR+Tb9DdCREQ63ZI9FXx1sJprRiXSL9YZ6HJEgo4CmYiIdKq8Kg8vrS3g5OQwLhoaG+hyRIKSApmIiHSa2nofC77IxWoxuH1iHyyGprcQaY4CmYiItGpfmYsnV+Xi9vrbdV65y8u9S3PYU+ri1tP6kBCmiV9FWqJAJiIiLfL5TZ76Mo/Psiv54kBVm88rqPYw++P9HKhwM2dyOqf3jezEKkW6PwUyERFp0fs7ysguc+O0WVi6p7xN5+wrc3H3R/updPu4f2pfxqdrvjGRY1EgExGRZhXV1PO/3xQxNjWcy0fEs6WwjtxKT6vnbCmoZc6SA1gMg4fPyWRYYlgXVSvSvSmQiYhIs15YU4DfhJvGJ/O9AVFYDFptJcut9HDfpznEhtp4ZHomGTGOLqxWpHtTIBMRkSa+OljFVwer+a+TEkiOsBMfFsLY1HCWZVfi85vNnvP6xiIM4P6pfUkM1wB+kfZQIBMRkUbq6v38eXUBmdEOLh4W17B9alYMZXVe1uZWNzlnd4mLLw5UcfGwOOL1NqVIuymQiYhII3/bVExxrZefnpqMzfKfecPGp0UQ7bSydE9Fk3Ne21BIpMPKJcPjmuwTkWNTIBMRkQZ7S128u72UcwdGNxmQb7MYfK9/NGsOVVNe523YviGvho35tVw+Ip6wEGtXlyzSIyiQiYgIAB6fn6dW5RHpsHLNqKRmjzknKxqfCcuyD7eS+U2T1zYUkRhmY8bgmK4sV6RHUSATEREAXl1fxP4KN7ef1odIR/MtXenRDoYmhLJ0TwWmafLlgSr2lLq48pRE7Fb9kyJyvPS3R0REWHuomvd3lHHhkFjGprU+keu0rGgOVXrYUljH6xuLyIx2cFa/qC6qVKRnUiATEenlyl1envl3HpkxDq4dnXjM4ydlRuK0GSz4IpfcqnquGpWA1aJFw0VOhAKZiEg35fWbLNxQxN5S13FfwzRNnvkyjxqPn19MSm1Tt2NYiJUzMqMorfMyLDGU8cdoURORY1MgExHppl5cU8A/tpTw+sai477Gv3aWsza3huvHJJHZjpn1zx8cS7jdwnWjkzAMtY6JnChboAsQEZH2+9fOMhbvKichzMb6vBoqXF6ine37lb6/3M0r6woZlxrO+e18QzIrzsn/Xj64XeeISMvUQiYi0s1szK/hhTUFjE8L5zdnpeM3YeX+qnZdI7vMxdxPDhBut3Dr6X3UyiUSYApkIiLdSG6lh0dXHCI9ys6dk1IZEOckM8bB8n2Vbb7GN/k1zFlyAIvFYN7UDGLa2bImIh1PgUxEpJuo9vh4YPlBDMPgN2elN8yKf1a/KHYU15FX5TnmNb7YX8nvPj1IfJiNR87NJKMd48ZEpPMokImIdAN+02TBylzyqzz8+sw0UiLtDfsmH5kD7PNjtJK9uSGXx1bmMjjeycPnZJIYrkXARYKFApmISDewPreGdXmH34Ycmdx4jcnE8BBGJIWyfF8lpmk2e/7/bS7mqeV7mZAewX1n921xJn4RCQwFMhGRbmDxrnJinFbOGxTb7P4p/Q/Pnr+n1N1k3+aCWv53YzHnDEnk7jPTcNj0q18k2OhvpYhIkCusrmdtbjXnZMUQYm3+bciJfSOxWQyW76totL223sfTX+aSHBHCXWcP1Iz6IkFKgUxEJID2lLr4zdIDlLu8LR7z0e5yAKYPanmusAiHlbGp4azYV4nP/59uyxfXFFJc6+WOiamE2dVNKRKsFMhERALokz3lbC6o5Y0WZtuv95ks2VPO2NSIYw7CP6t/FGUuH5sKagH4KqeKT/ZWcOnweIYmhnZ47SLScRTIREQCaG1uDRYDluyuYE8za1L+O6eKCpePGa20jh01Pi2CsBALy/dVUO7y8uxX+QyIdfBfJyV0Ruki0oEUyEREAiS30kN+dT0/OjmBKIeVF9cUNHlLcvGuMpIjQhidGn7M69mtFiZmRPLlgWqe+TKP2no/d0xMbXHcmYgEDwUyEZEAWZtbDcCZmVFcNSqRrUV1rPjWEkgHyt1sKazjvIExWNq4tNFZ/aKo8/pZm1vD1aMSNfGrSDehQCYiEiDrcmtIjQyhT6SdqQOiGRDr4C/rC3F5/QB8uKsMm8VgalZ0m685IimM5IgQTkoO46KhzU+RISLBR4FMRCQA3F4/mwtrGZMaAYDVYvDjccmU1HpZtLWEuno/n2ZXckZGJNHtWGvSajF4/Lx+/PZ76W1uVRORwNOKsiIiAbClsBaPz2Tst8aGDU8K48zMSP65tRSfH2rr/Zw3+NiD+b8rSrPwi3Q7aiETEQmAtbk12K0GI5IaL4N07egkAP6xpYR+MQ6GJmi6CpHeQIFMRCQA1uVWMzIprMkyRonhIfxgRDwA5w2KwVC3o0ivoC5LEZEullflIbeqnvMHNz/o/tLhcSSGh3BmZlQXVyYigaJAJiLSxdbl1gAw9siA/u8KsVo4e0Db36wUke5PXZYiIp1g8c4yFqzMpd7nb7JvbW41KREhpEbZA1CZiASjLmsh27BhA6+88gp+v5+pU6fy/e9/v9H+4uJinn32WWpqavD7/Vx55ZWMGTOmq8oTEekwBdUeXlpbSL3fxGLAzyf2aRgL5vH52VRQyzntmFtMRHq+Lglkfr+fl156iXvuuYf4+Hhmz57NuHHjSE9Pbzjmrbfe4vTTT+fcc8/l4MGDPPzwwwpkItItvbKuCIsBFw6J5f0dZaRG2bniyHqSWwrr8PjMhvnHRESgi7osd+/eTUpKCsnJydhsNiZOnMjq1asbHWMYBrW1tQDU1tYSG6sZpkWk+/kmv4Yvc6r4wYh4bhybxNkDovjfb4r5fF8lcLi7MsRicFJy2DGuJCK9SZe0kJWWlhIfH9/wc3x8PLt27Wp0zOWXX84DDzzAhx9+iNvt5t577+2K0kREOozPb/Li2kKSwkO4eFgchmFwy4Q+FFbX88yXeSSG21iXW8OI5KbTXYhI7xY0b1l+8cUXTJkyhYsuuoidO3fy+9//ngULFmCxNP6ltXTpUpYuXQrA/PnzSUhI6PTabDZbl9xH2kfPJXj11mfzz2/y2F/u5oHzh5KW8p/P/+j3Y7jp/zby4PJDVLl9XDYqLSDfT299LsFOzyV4deWz6ZJAFhcXR0lJScPPJSUlxMXFNTpm2bJlzJkzB4DBgwdTX19PVVUV0dGNB75OmzaNadOmNfxcXFzciZUflpCQ0CX3kfbRcwlevfHZVLl9PL9qHyOTwxgZYzb5/HPOTOWuj/YBMCS6a353fVdvfC7dgZ5L8OroZ5Oamtrivi5pM8/KyiIvL4/CwkK8Xi+rVq1i3LhxjY5JSEhg8+bNABw8eJD6+nqiojQpooh0D3/dVEyNx8ePxyY1O7t+apSduWf35YYxSaRFaroLEWmsS1rIrFYrN9xwAw8++CB+v5/vfe979O3bl7///e9kZWUxbtw4rrnmGp5//nk++OADAG655RYtGSIi3cKBcjeLd5YxfWAM/WKdLR43KD6UQfFam1JEmuqyMWRjxoxpMo3FFVdc0fDn9PR05s2b11XliIh0CJ/f5I9f5xMWYuHKUxIDXY6IdFN6zUdE5AS8vrGIrUV1/HhcMlEOa6DLEZFuSoFMROQ4fXWwikVbSzlvUAxT+mvmfRE5fgpkIiLHIb/Kw9Or8siKczJrbFKgyxGRbk6BTESknTw+P4+sOIRhwN1npmK36lepiJwY/RYREWmnF9cUsrfMzc9PTyU5QlNYiMiJUyATEWmHZXsr+Gh3OT8YEc/4dC0QLiIdQ4FMRKSNXF4/L64tYERSKFeerKVuRKTjKJCJSK9TWF2PaZrtPu+z7ApqPH6uOiURq0UTV4tIx1EgE5FeZWthLT9+Zw8vrC1sVygzTZMPdpQxINbBsETNti8iHUuBTER6lXW5NQB8sKOMl9a1PZRtKqjlQIWHC4fEalk3EelwXbZ0kohIMNhcWMugeCdDE0J5b3sZVsPgutGJxwxZ7+8oI8ph5cx+UV1UqYj0JgpkItJruLx+dpXUMXNoHNeMSsRnmry9rRSLAdeMajmUFVR7+PpgNT8YEa85x0SkUyiQiUivsb2oDq8fTkoOwzAMfjIuGb8Ji7aWYjUM/vuUhGZD2b92lmMYcN7gmABULSK9gQKZiPQamwtqsRgw9MigfMMwuGl8Mn7T5M0tJdT7zSbdly6vnyV7yjm9byQJYSGBKl1EejgFMhHpNTYX1jIwzklYiLVhm8Uw+OmEFKyGwdvbSql0e/nZqX2wHZnW4uhUFxcNiQ1U2SLSCyiQiUiv8O3xY99lOdJSFhNq46/fFFPp8nHXmWnYrQbv7ygjK87R0KomItIZNDpVRHqFb48fa45hGPzXSQncPD6Ztbk13PtJDqsOVJFT4eHCIXGa6kJEOpVayEQkaHy6t4LE8BBGthCaTsR3x4+1ZMbgWKKdVhZ8kcejK3OJclg5IzOyw+sREfk2tZC1wjx0AN9js6nftzvQpYj0eKZp8uc1Bfx9c3GnXH9TQdPxYy2ZmBHFfWenE263cPGwOE11ISKdTr9lWhMZBbu24V61LNCViPR4hTX11Nb72VPqOq51JltzdPxYe1reTkoO57XLBnHZ8KZjzkREOpoCWSuMqBgYMhLXqmUd/g+EiDSWXeYGoMbjJ7+6vkOvvb2oDp/Z8vixltgshsaOiUiXUCA7BmPsJHyHDkDugUCXItKjZZe5Gv68u8TVypHtt+nI+LFhiR0/Nk1EpCMokB2DMeY0sFgw13wR6FJEerTsMjcpESGEWAx2l3ZsINtccHj9ytAQ/coTkeCk307HYETFEjJ8FOZaBTKRzpRd5mZgvJN+sQ72dGAgaxg/lqTWMREJXgpkbeCc+D3Iy8FUt6VIp6j2+Cisqad/jJOBcU72lLrwd9C4zaPjxzpjKg0RkY6iQNYGjtOmgGGo21Kkk+w/MqC/f6yDrDgntfV+8qs6ZmC/xo+JSHegQNYG1th4GDRc3ZYinWTvkQH9/WIdDIx3AnTYODKNHxOR7kC/odrIGDsJcg9g5uUEuhSRHmdfuZtoh5W4UBt9ox3YrQa7S+pO+LoaPyYi3YUCWRsZY04/3G2pVjKRDpdd5qJfrAPDMLBZDPrFdMzA/jWHqg/PP5YS3gFVioh0HgWyNjJi4iFrmMaRiXQwr9/kQLmH/rHOhm0D453sKXWf0MD+rYW1PPNlHhnRdkYktb5+pYhIoCmQHcP2orqGWfqNcZPg0H7M/IMBrkqk5zhU6aHeb9I/1tGwLSvOSZ3XT26V57iuuaO4jvs/PUh8WAj3T83QWpQiEvT0W6oVWwpqufvj/TywZBdurx9jzEQAzLWrAlyZSM9xdIb+Ri1kcUcG9h/HjP17Sl38blkO0U4rD0zrS2yorWMKFRHpRApkrRiWFMqPTkrgo22F3P3xfgpDIiFrqLotpcfYUlDLhryagNaQXebGZjFIi7I3bGsY2N/OcWT7ylzM/eQAYSEW5k3NID4spKPLFRHpFApkrbAYBv91cgKPzhxOYU09dy7ex/qTzoGD2eq2lB7hpXUFvLCmIKA17CtzkRljx2b5zyLeVotB/1gne9rYQubzm6w+WM1vP8nBbrXwwLQMkiIUxkSk+1BbfhtM7B/HgvP6Mf/zQzxQ3pcr+p3DD+6/A8tJY7GMPwNOGofhcB77QiJBxOs32V/uAUx8fhPrtwJRVzFNk+wyN+PSIprsGxjn4JO9Fa3Wtq/MxafZlSzPrqDM5SMhzMb9UzNIibQ3e7yISLBSIGujPpF2Hp2eybNf5fM3zmF7v7Hc+s1CYtetArsD45QJGGdfgDFweKBLFWmTgxVuvP7DL6wUVNeTGtX1IabM5aPC7Ws0oP+ogfGhfLCznENVHjKiG+//Jr+Gl9cVkl3mxmrAuLQIzh4QzdjUCEKsXR8sRUROlAJZOzhsFu6Y2IdhiaG8vM7gzvF3cluGmzE7P8dcuwpz9QqMsZMwLrsWIzEl0OWKtGrvkeWKAA5WugMSyLKPjBEbENu0hfnowP49Ja5Ggayktp5HVhwiwm7lx+OSmJwZRZRTv8pEpHvTGLJ2MgyDGYNjWXBeP2JCbczbZeOVYZfhe/AFjIt+hLlpNf7f/gz/otcwXbWBLlekRdllLmxHfgMcrDy+6SVOuIbyw6Ews5kWsrQoO47vDOw3TZPf/zsfj8/kt9/ry4VD4hTGRKRHUCA7ThkxDh6bnskFg2N4d3sZd3+WT/6Uy7DM+xPGuDMwF/8D/29uxlz/70CXKtKsvWVuBsQ6iXZaORSoQFbmIik8hAi7tck+q8VgQJyz0Yz9H+4qZ31eDdePSWr0VqaISHenQHYCHDYLPxmfwm/OSqOo1svdH+9ntxmOZdYdWOY8DrEJ+J9/BHPTmkCXKtLI4cH0LvrHOkmPsgcwkLmbHT92VFack72lLnx+k9xKD6+sK2R0n3BmDIrpwipFRDqfAlkHmJAeyWPTMwkNsXDP0gOsz6vB6D8Yyy8egLR++P80H3P31kCXKdKgsKaeGo+f/rEO0qLsAemydHn95FZ6Wg1kA+OcuH0mByrcPLkqlxCrwa2npWAYGrgvIj2LAlkH6RNpZ/65mfSJtPPAZzl8vq8SIzQMy+1zISYB/+/nYR7aH+gyRYDDLVMAA+KcpEc5qHL7qHR5u7SG/eVuTKBfMwP6j8qKP7zvmS/z2Fni4qbxKZrsVUR6JAWyDhQXauPBaRkMTQhlwRe5vLe9FCMqBssdvwO7A/9TczGLAzsJpwgcHrtlMaBfjKNhLFZXd1vuOxoKW2khS4u047QZ7C1zc2ZmJJP7RXVVeSIiXUqBrIOF263MPbsvp/WN4MW1hbyyrhBfXBKWn/8OPB78T/4Ws7I80GVKL7e3zE1qpB2HzUL6kUDW1d2W2WUuwkIsJIW33OJltRgMjg8lLtTGTeM1lYyI9FwKZJ3AbrVw1xlpnD84hre3lXLv0gOUxqZiufVeKC/B/8S9mGUlgS5TerHsUlfD3F+J4SGEWIxWA9mKfZXc9n42tfW+jquhzE2/GMcxx4PdOSmVx8/LJNLR9E1MEZGeQoGsk1gtBjeNT+EXk1LZW+bi5//ax/qwvlj+514oKcT/8K8wD+4LdJnSC1W5fRTVehsG01stBqmRrb9p+cWBSvZXuFm6p6JDathd4mJvmYv+ccdeciw21KZxYyLS4ymQdbLJ/aJYcF4/Yp027v/0IP/rScX81cNg+vE/+mvMbRsDXaL0Mtllh+f1+nYYSou2c6jS3ezxpmmytbAOgHe2lTYst3S8NuTV8JulB4hxWpk5JPaEriUi0lMokHWB9GgHj52XydSsaP5vcwn377Lh+uUjEJeI/+nf4f/y00CXKL3I0Tcsvz3dRFqknfzqeup9TcPWoUoPFW4fp6ZHUFzrZeX+yuO+94p9lcz7LIfkiBDmn5upRcBFRI5QIOsiDpuFW0/rw62npbC5oJbfrK2l4rYHYOAwzJefxP+vNwNdovQSe0tdxIXaiPnWkkPp0Xb8JuRXN+223Fp0uHXsmlGJZETbWbS1FNNsfyvZ+ztKWfBFLoPjQ3nonAx1Q4qIfIsCWReblhXDPVPSya308OsVxeTPmoMx4SzMfy7Ev+SdQJcnvUB2mbvJVBNprbxpuaWwlminlbQoO5cMj2d/uZt1uTXtuucbG4t4YU0hE9IjuO/svs0ulSQi0pu1OZBt3ryZwsJCAMrKyvjDH/7Ac889R3m5pnBorzGpETwwLYO6ej+zP8llz/dvhjETMf/vJfyrlgW6PAmgCpeXf+0sY/bH+7nqH7soqa3v0Ot7fH5yKt30/85krA1zkVU000JWWMfwxFAMw+DMzCjiw2ws2lba5ntuLqjl/zaXMHVANHefmYbDpv8fKCLyXW3+zfjSSy9hsRw+/LXXXsPn82EYBs8//3ynFdeTDU4IZf65mThsFu5ZdpCNF/4Uhp2C+eozmBu+CnR50oV8fpNleyu4b1kO1y3azfOrCyhzealy+1jbzpaoY9lf7sZvQv+4xi1kYSFW4kJtHPzOwP7i2noKa+oZnhQGQIjV4OKhcWwuqGVncV2b7vnm5mJinFZuGp+M1aIlj0REmtPmQFZaWkpCQgI+n4+NGzdy00038eMf/5idO3d2Zn09WlqUnUemH1luaUUen8y4FTKy8D//KOaOzYEuT7rIv3aW8fSXeRyq9HDJsDiePr8ff7xoAPFhtnZ3DR5Lw5JJzSxX1Nwi40ffrhxxJJABnDMwmvAQC/9sQyvZrpI6NuTXcvGwOLWMiYi0os2/IUNDQykvL2fr1q2kp6fjdB7+he71du36dz1NXKiNh87J4OSUcJ5dV8ob0+7An5iC/9kHMA/sCXR50gW2FdWREhHCny8ewDWjk+gX68QwDEb3Ceebghp8JzjNxLftLXURarOQHNF0QH3akUD27QH7WwtrCbVZ6Bfznxa1sBArMwbH8uWBKvKqWp/d/x9bSgi3WzhvUEyHfQYRkZ6ozYHsvPPOY/bs2TzzzDNMnz4dgO3bt5OWltZpxfUWYSFW7p2SzvSBMby1u5onzvoF7rBo/E/dh1muGf17uuwyF/1jm85YP7pPODUeP7tKXB14Lzf9Yx1YmpkdPy3KTk29n3LXf2bj31pYx9DE0CZdjRcOicVmMXi7lVayAxVu/p1TzYVDYgkL0SB+EZHWtDmQff/73+fee+9l3rx5TJo0CYC4uDhuvvnmTiuuN7FaDH46IZnrxyTyZb6H+yb9gnK/Ff+LT2D6O265GgkutfU+8qrqmwyyBzg5JRyDwxOpdgS/abKvvOXZ8dOjD7eCHe22rHL72F/hZnhSaJNjY0NtnD0gmk/2VLC/vPkJZd/aUoLTZnDhkLgOqV9EpCdr16CO1NRUUlIOL/C7efNmysvLycjI6JTCeiPDMPj+sHjunpxGdo3J7NN/SWn2fszFbwW6NOkk+8vdmDSepPWoKIeVgfFO1ndQIMurqsflNZtMeXHUfxYZPxywthbVAjAiMazZ4y8fGU+kw8q9nxwgp6JxKMutcPH5vkqmD4whSmtQiogcU5sD2dy5c9m+fTsAb7/9Nk8//TRPP/00ixYtatP5GzZs4Pbbb+fWW2/l7bffbvaYVatWcccdd3DnnXfy9NNPt7W0Huf0vpE8OC2DCkJ44NRbqfngLcw92wNdlnSCfQ2z5jffajW6Tzg7S+qo9px4K2nDkkkt3Cs+zIbD+p9FxrcW1mGzGAxKaP74xPAQ5k3riwHcu/RAoxcC3lh7EIthcPEwtY6JiLRFmwNZTk4OgwcPBuCTTz5h7ty5PPjggyxZsuSY5/r9fl566SXmzJnDk08+yRdffMHBgwcbHZOXl8fbb7/NvHnzeOKJJ7juuuva90l6mMEJocyenE6OLZpHT7kB94tPYNZWB7os6WDZZW4i7BYSwmzN7h/dJxy/CZvyazvkXlYDMqKbX67IYhikRtnJbQhktQyOd2K3tvxrIj3KwbxpGfjNw6Esr8pDaZ2XD7YWMHVAtGbjFxFpozYHsqNvXuXn5wOQnp5OQkICNTXH7k7ZvXs3KSkpJCcnY7PZmDhxIqtXr250zCeffML06dOJiIgAIDo6us0foqca1Sec/zmtD99EZPJc4hT8rz17XEvWSPDaW+ai/5G3KpszOCGUUJulQ7ot95a66BvtIKTVgGXnYKUHl9fPnlJXw/xjrcmIdnD/1L54fH7uXXqAv6wrxOc3uWS4WsdERNqqzYFsyJAhvPzyyyxcuJDx48cDh8NZZGTkMc8tLS0lPj6+4ef4+HhKSxu/nZWbm0teXh733nsvv/nNb9iwYUNbS+vRzh4QzX+fksDy5DG8XhqJufLYLZLSPfj8JvvL3fRrYUwXgM1icHJKGOvzak4ojHv9JjtK6hgY33z341HpUQ4Kq+vZXFCLz4ThiU0H9DenX6yT303NoNbrZ/m+SqYOTqSPFg4XEWmz5vtJmvGzn/2M9957j6ioKGbOnAkcDlHnn39+hxTi9/vJy8tj7ty5lJaWMnfuXB5//HHCw8MbHbd06VKWLl0KwPz580lISOiQ+7fGZrN1yX1a8tOz4qn2WVnE2cR/9h5XDh6GRntPyAAAIABJREFUfcTogNUTLAL9XE7U/tJaPD6Tk/smtPo5zhxUz1ef7sFli6BvbNsC0netO1hOjcfP94b2afVeQ9NNzE3FrDhYhwFMGppOhKNtvyYSEuDpqGj+tGofP5nYn4QoBbJg093/zvRUei7BqyufTZsDWWRkJFf+f3v3HR5llf///3lmUkghZZKQkAACoUmRYpCiSIu999XVj21XV1zrWrAurrJrB3V1dV1sX/Unuq6orIgCAgqiICCKdEJNIKRAEtJzn98fA5EsCQQIuSfJ63FdXsnMnJn7PRxvfXHuc59zxRU1nhswYEC93uvz+cjN/XU9rdzcXHw+335tunbtSlBQEG3atKFt27ZkZWXRpUuXGu3S09NJT0+vfpyTk1Pfr3DY4uPjG+U4B3J1nxgyd+TzL3s2bZ97kQG/uQhz3EBXa3JbIPTLkfhhQwEACcEVB/weXVr7R8Zm/bKFs7rHHtaxZizfTpDHkBrhHPBYUfhvMvh6XS4dY0MpLdxJaWH9j5MQBA+d3Jb4qJAm3TfNVVM/Z5or9Uvgaui+SU5OrvO1el+yrKys5P333+ePf/wjv/3tb/njH//I+++/X6+V+lNTU8nKyiI7O5vKykrmz59PWlpajTYnnHACy5cvB6CgoICsrCwSExPrW16z5/UY7hrRkQ5RwTzb4zdsm/QSzndz3C5LjsCG/FKCPP7LhAfStnUISZHBhz2PzFrL91uL6JMYTljwgU/5vZuMV1nqNX9MREQaRr1HyN5++23WrVvH73//exISEtixYwcffvghxcXFB70j0uv1ct111zF+/Hgcx2HkyJG0b9+eyZMnk5qaSlpaGn379uXHH3/kjjvuwOPxcOWVV9ZrflpL0irIw9gRHfjTtAye6nc941+bQGhxEZ6RZ7ldmhyGjPyyPZPsD77hdv+2EXyVUUBFla1X+31tLSgnq7CCc+qxQGtokIc2EUFk766kVz3nj4mIyJGrdyBbsGABTz31VHVISk5OplOnTtx99931WqJiwIAB+13ivOyyy6p/N8Zw9dVXc/XVV9e3pBYpOSqE24cm89e5lkmDruemd1/G2V2IOeuyOu/Uk8CUkV9K/+SIgzfEf8fttDU7WZVTQu/EQxu5+n6rf7mUE9pF1qt9clQo2bsrNUImItKIDnnZC3HfoPatuainjy9DOzPrxCuxH7+LnTfD7bLkEOwsqSS/tIqOMQe+63Gv45LC8RgO67Llwi1FdIoNJSGifmuCnZASycCUCGLD6v33NREROUL1DmRDhgzhiSeeYOnSpWzZsoWlS5fy1FNPMWTIkKNZn9Tht30TOC4pnH+26sv6nsOwH7yGLch3uyypp4yde1foP/D8sb3Cg730iA875H0tC8qqWJlTwsCU+o2OAZzVPZYHR7Q/pOOIiMiRqXcgu/LKK+nTpw+TJk1i7NixvPbaa/Tq1YugIP0t2g1ej+GuE5NpHerlqQ7nUuh4sO/9y+2ypJ4y8g68jVFt+reNYF1eKQWlB7+RZq8fthbhWA4pkImISOOrdyALCgrisssu44UXXuDtt9/m+eef58ILL+TTTz89mvXJAUS3CuLeYSnkllsmnnQblQu/wS5bePA3iusydpYRHx5E60PYePv4lEgsMHP9rnq/Z+HWImJbeQ+6IKyIiLir3oGsNppE7r7u8WH8Pi2RJZVRvN/nIpx3/oEtPfJ9D+XoytizZdKhSPW1on/bCD5cnsvuemw2XlFlWZy5m7SUSDw6V0VEAtoRBTIJDKd1iWF052g+iDuB7z1tsFPecbskOYCySoetBeX1nj+2r6v6JVBY7jBlRd5B2y7PLqak0mFgPe+uFBER9xx0AtjPP/9c52v1WRRWjj5jDDcOTGTDzjKe730lT377LO0GDcd06uZ2aVKLTbvKcGz9J/TvK9XXipOOac3HK/I4s1vsAe+E/H5rESFeQ7+k+i2tISIi7jloIPvHP/5xwNe1/1ZgCA3yMHZYCndOy+CJPtfy+FsvE/HAkxjddBFwMvL33mF5ePO6fntcAvM3FfL+zzncODCp1jbWWhZuKaJvUjihQRoIFxEJdAf9v/WLL77YGHVIA2gTGcxdJ6XwyKwqXowaxF2f/H94L7zK7bLkf2TklxIW5CExsn7rgv2v5KgQTkmNYfqanZzXw0dS6/038d60q5zs3RVc3CvuSMsVEZFGoL86NzP92kZwZb82zG/Tl7dW7cZZssDtklqEn7bvZkV2/W6m2JBfRsfY0COaaH9Znzi8HsO7y2rf9HbhFv/q/GkpulwpItIUKJA1Qxf29HF6ahRTOozg77PXU5m5xe2SmrWySofxs7cy9stN/GvRdsoqnTrbOtaSkV92WPPH9hUXHsw53WOZu6GAjPzSGq/lFlfwzaYCuvhaERd+eKNwIiLSuBTImiFjDH8Y1JZLU1sxs80AnvzkR8p2aymMo2Xh1iJKKh2OT47g01X53DltA+vySmttu72ogpJK57Dnj+3rwp5xhId4eHvpDnaWVvLZ6nzu/3Ij13+0joz8MtJTo4/4GCIi0jgUyJopYwy/HdyR61PK+a51Kn/5cDG7y3VX7KEqqah7tGuvORsKiA0L4oHh7Rg3qj3FFQ53f76B93/OocqpuQfshvxD2zLpQCJDvVzUM45Fmbu59j9reWXhdnaVVvGbPvG8eHYnzugWe8THEBGRxqFb8Jq5c0ccR+RHX/LC7mQe+uhn/nxeb6JbqdvrY8WOYh6asZlrB7ThrO61h5uCsioWZxZxdncfXo+hf9sInj+rEy8v3MY7P+bw0S95RAR7CA3yEBbsobCsCo+BDtFHHsgAzu4ey4adZSRFBnPSMVF0iA7Rgs0iIk2QRshagJHnpzN293dsLvPy3Iw1bpfTJBSWVfHMN5lUOJYPl+dSUWVrbTdvYwGVDgzvGFX9XOtQL3eflMK9w5IZ0SmKPkkRdIgJpXWIF19YEGd2i22wpShCgzz86cRkfts3gWNiQhXGRESaKA2VtADGGE74v99w+Ytv8aZ3BN9t2sWgDppfVBdrLS8syCK/tJLfHhfPO8tymLNhF+mpMfu1nbuhgHZRIbVeghzaIYqhHaL2e15EROR/aYSshTBh4Zwz4jja7d7OpPmbDngnYEv32eqdfLeliP/r14ZLesfRKTaUj37Jw7E1R8m2F5Xzy44SRnSK0siUiIgcEQWyFiTo+CH8vngJ26uC+ein7W6XE5DW55Xy2uJs0pIjOLdHLMYYLjjWx5aC8uq1vfaau6EAgJM7ahRMRESOjAJZC2KMoe+5ZzA0+0c+/CWf7UXlbpcUUIorqnjqm61Eh3q5bUjb6lGvk46Jok1EMB/+kofdM0pmrWXOhgKOTQgjMXL/lfJFREQOhQJZC2M6d+fayGxMVSWTFhzZgrHfbSnk7s83UF4VuJc/N+8qY+qqX4NUXay1vPL9drYVVfCnE5OJ2udOVK/HcP6xPlbllLBiRwng349y867yGpP5RUREDpcCWQuUcP4lXLJ5Ft9tL2dxZtHB31CHD5fnsTq3lJ+2BeaisznFFTw8czOvLsrmx4PUuHRbMbM3FHBZ73h6JYbv93p6ajRRoV7+80su4F97zGvgxGMUyERE5MgpkLVAJiGJc7vG0LZ4B68u2ErFYYxwbdpVxqoc/2jR91sPP9QdLcUVVTw2ewslFQ6tQ718sjLvgO3/80suvrAgLqpjM+7QIA9ndY9l4dbdbMgvZe6GAgYkRxIV6j0a5YuISAujQNZChZx1Mb/b/AWZJZYPfs495PfPWLuTIA/0ahPGwi1FB70k2JiqHMvT32SycWcZ9wxL5uzusfyQuZvNu8pqbb8ur5Rl24o5p3sswd6675Y8s1ssoV7D0/MyySupZEQnjY6JiEjDUCBroUxEJAOGn8DwbT8w+edcpq/ZWe/3VlRZvsooYGBKa9JTY8gtqWR9fu1hp7FZa3l10XZ+yNzNjQMTGZAcyeldYwj2GD5dmV/re6b8kkdYkIfTuu6/zti+okK9nNolhs27ygkL8jAwJfJofAUREWmBFMhaMDP8dMbsWsCA3JX847ssvpo6G1tUcND3fb+1kIKyKk7tEs3xyREY2G9JCLd8sjKfaWt2csGxPk7v6t/uKKZVECM6RfFVxi4KSmvu57m9qJxvNhVwWtcYIkIOfvnxvGN9eA0M6dC6wVbbFxER0f9RWjATFEzo2L9xT69gepVm8vzOBOb/7UmqXn4cm1H3Fktfrt1FfHgQfZMiiG4VRI+EML7fWlhn+yrHMm9jAcUVVUfja1RbtLWI1xdnM6R9a/6vf0KN187t4aO8yvL52pojgZ+szMcA5/So30bcCRHBPHHaMVw7oE1DlS0iIqJA1tKZ8EjCRp/FA1ePoGtUEM/2vIIlWbtxJj6MLdy1X/sduytYmrWb0anReD3++VYDUyJZl1dGTnFFrceYuX4XT36TybhZmykqP3qhbPJPObRtHcIdQ9vi+Z+V8zvEhNK/bQSfrd5ZvS9lYVkVX67dyckdo4gPD673cbrGhWkyv4iINCgFMgEgPNjLw6d1oX1sGE/0+C3LQ5OwH765X7uZ6/whbXTnX/fCPKGdfy5VbZctKx3Lv5fn0iYimHV5pTw8cxMFZQ0fyrbsKmN1bimndY2u81LiuT1iyS+p5JuN/suy09bkU1ZlOf9YX4PXIyIicigUyKRaZKiXcaPa0yYyhEf7/o6FK7di16+qfr3KscxYt5O+SeE1VqdvFxVCUmQwC2tZ/mLuhgK2F1VwQ1oi953cjk07y3lwxiZ2/s9criM1a/0uPAaGd6x70/T+bSNoHx3CxyvzKKt0mLoqnwFtI+gY26pBaxERETlUCmRSQ0yrIMand6BDbBiP976azz+dg3X8I1rLthezo7iSU7rUvBvRGMMJ7SJZtq2Y0n02La9yLB/8nEPn2FDSUiJIS4nkwRHtyCos54EvN5FbxyXOQ1Xl+O/6HNA2gtiwoDrbGWM4t4ePjPwyXvxuG7tKq7igp0bHRETEfQpksp+YsCAeO6Uj/SIqeDn+ZN6ZuhBrLV+u3UnrUC+D2u2/3MPAlEgqHMvSrN3Vz32zsYDMwgou7R1fvS9kv7YRjBvZnpziSh6YsYnXF2fzwoIsHp+7lYdmbOLOaRt4Zl4mszN21fvS5rLtxeSVVDIqte7Rsb2Gd4wiKtTLnA0FpPpa0aeWVflFREQamwKZ1Cos2MP95/Zh9O41fFAYw7NzN/HdlkJGdIoi2Lv/vzY924QTEeLh+z3zyBxref/nXI6JDmVQ+5oBrldiOI+Mak9JhcO01fksztzN1oIyKhxLVKiXH7N2M2F+Fld/uIZ7pm/k/Z9yDjiaNmvdLiJDPJxQj3XBQoM8nNHNP8J3wbG+6qAoIiLiprqv70iLF+z18Mcz++B76798wGgATkmtffHUII/h+LaRLNpaRJVjWbC5kC0F5dx1YvJ+dzwC9EgI440Lu9QaiBxrWZtbyg+ZRfyQuZt3luXwVUYBE8/suN+E/d3lVSzYUsjoztG1BsXaXNgzjraRIZx4TOt6tRcRETnaNEImB+Rp15ErUkO5dcV7XNrOcExMaJ1tB7aLZFdZFatzS3j/51xSokIY2qHu0FPX6JTHGLrFh3H5cQk8fXpHHhnVnszCct5ckr1f23mbCimvsozqfPDLlXu1CvIwsnN0rUFRRETEDQpkclDmnMsZUbKe38x7FevUvRH5gLYReA28umg7G3aWcWnvuOq1yo5Ev7YRnNM9lv+u3lljjhr4765sFxVC1zjdKSkiIk2XApkclAmPwFx0NWSsxn43p852kaFeerYJZ11eGUmRwQw7puE2376qXwLtokJ4/tssivZM9t+cX8KKHSWM6hytuWAiItKkKZBJvZjBI6FjV+yHb2JLS+pst3eR2EsaaHRsr9AgD3cMTWZnaSWvLNoOwLSV2XgMjOjUcMFPRETEDQpkUi/G48Hzm9/DrjzstA/rbHdKagw3D0piZKf6z+mqry5xrbisTzxzNxQwd0MBn6/Ipm9SBHGHsO2RiIhIIFIgk3ozqT0wg4Zjv/gIu2NbrW3Cgj2c2iWmQUfH9nVxrzi6xrXiuW+z2F5YdkiT+UVERAKVApkcEnPh1eDx4Pz7DVeO7/UY7hiajMdAZEjti9SKiIg0NVqHTA6J8cVjzrgI+/G72FU/Ybr3afQaUqJCuHdYCq0iIgnVv8EiItIMaIRMDpk59QLwJeC892r1PpeNLS0lkhFd4l05toiISENTIJNDZkJCMRdfC1s2YL/+0u1yREREmjwFMjksJu1E6NoTO+X/1TnBX0REROpHgUwOizEGz1U3g2Nxnn0Im5/rdkkiIiJNlgKZHDbTtj2e28dBYQHOhIexhQVulyQiItIkKZDJETGduuG55UHI2Y4z8c/Y4t0Hf5OIiIjUoEAmR8x074PnprGwdQPOC49iy8rcLklERKRJUSCTBmH6pGGu/xOsW4nzj79iq9xZDkNERKQpUiCTBuMZeBLmihth+RLswq/dLkdERKTJUCCTBmVOPg3atsdO/whrrdvliIiINAkKZNKgjMeDOfV82JIBK350uxwREZEmQYFMGpwZNAKiY3Gmf+R2KSIiIk2CApk0OBMcjBl1NvyyBLslw+1yREREAp4CmRwVZvgZENoKO32K26WIiIgEPAUyOSpMRCTmpFOwC+di83LcLkdERCSgKZDJUWPSzwXHYmd96nYpIiIiAa3RAtnSpUu57bbbuOWWW5gype7LWAsWLODSSy9l3bp1jVWaHCUmPhGTdiJ27nRsSbHb5YiIiASsRglkjuMwadIk7r//fiZMmMC8efPYsmXLfu1KSkqYNm0aXbt2bYyypBGYU8+HkmLs19Orn7OFBTgLv8Z571VsdqaL1YmIiASGoMY4yNq1a0lKSiIxMRGAoUOHsnDhQtq1a1ej3eTJkznvvPP45JNPGqMsaQSmY1fo3gf75Sc4u4uwy5fApnWwZ9FYm52F99aH3S1SRETEZY0SyPLy8oiLi6t+HBcXx5o1a2q0Wb9+PTk5OQwYMOCAgWzGjBnMmDEDgMcff5z4+PijU/Q+goKCGuU4zVXZpdey89E7sZ//h+BuvQi57HpC+p1A+bJF7H73n0TlZBHSo88hf676JXCpbwKT+iUwqV8CV2P2TaMEsoNxHIe33nqLMWPGHLRteno66enp1Y9zco7+HXzx8fGNcpxmq0MXPA9NgPgknPAISoFSwA5Nh08nk//WS3jvfPSQP1b9ErjUN4FJ/RKY1C+Bq6H7Jjk5uc7XGmUOmc/nIzc3t/pxbm4uPp+v+nFpaSmbN2/mkUce4eabb2bNmjU8+eSTmtjfjJgOqZjwiJrPhbbCnHkxrPgRu3KZS5WJiIi4r1FGyFJTU8nKyiI7Oxufz8f8+fO59dZbq18PDw9n0qRJ1Y/HjRvHVVddRWpqamOUJy4yw8/ATp+C8/E7eLr3wRjjdkkiIiKNrlFGyLxeL9dddx3jx4/njjvuYMiQIbRv357JkyezaNGixihBApQJDsGcdSmsXQHLF7tdjoiIiCuMtXtud2uiMjOP/rIJur5/dNnKCpwHb4LIKDwPPFPvUTL1S+BS3wQm9UtgUr8ErmY3h0zkQExQMOacy2HjWlj6ndvliIiINDoFMgkIZvAISEzB+fgdrOO4XY6IiEijUiCTgGC8Xsy5l8PWjdiZn9LEr6SLiIgcEgUyCRgm7SQ4ti/2/Uk4rzyBLSxwuyQREZFGoUAmAcN4PHhuH4e58GpY+j3OI7dgf9JduCIi0vwFxEr9InsZjxdzxkXY3gNwJj2L8/xfMCefhkk/19/AccA64Fic0BB3ixUREWkgCmQSkEz7TngeeBb78dvYL6Zg507fr82O0FaY9HMxp12ICQt3oUoREZGGoUAmAcsEB2MuvhZ7wsnYrC3g8WA8HtizTlnwT4so++/72DmfY866BDP8TExwsMtVi4iIHDoFMgl4pkMqpsP+22jFnHouO0acifPhm9jJk7AzPsVz2e8w/Qe7UKWIiMjh06R+adLMMV3w3vkonjsegbBwnFeexO7MPfgbRUREAogCmTQLpmd/PGPuB8fBzpzqdjkiIiKHRIFMmg2TkIQZMAQ753NsabHb5YiIiNSbApk0K+a0C6FkN3buF26XIiIiUm8KZNKsmE5doXsf7IxPsJWVbpcjIiJSLwpk0ux4TrsA8nOwC792uxQREZF6USCT5qf38ZDcATv9P9qkXEREmgQFMml2jDGYUy+ArRth+RK3yxERETkoBTJplsygkyHGh/PFR26XIiIiclAKZNIsmaBgzOhzYMWP2I3r3C5HRETkgBTIpNkyJ58OrcKwGiUTEZEAp0AmzZYJj8CcfDp24dc4X0zRBH8REQlY2lxcmjVz7hXYnG3YD16DbVvgihsxQcFulyUiIlKDRsikWTOhoXhuvBdz5qXYr7/AmfBnbFGB22WJiIjUoEAmzZ7xePBccCXm+jth/Sqcv96FzdridlkiIiLVFMikxfAMHoHnrvFQWoLzt7uxK350uyQRERFAgUxaGJPaA88Dz0BsHM5zj+B8N8ftkkRERBTIpOUxcW3w3Ps4dDkW+69ncKZ9qDswRUTEVQpk0iKZ8Eg8t43DDByG/c+b2HdfxjpVbpclIiItlJa9kBbLBAfD7/4EvgT/RuT5uXiuvgXTOtrt0kREpIXRCJm0aMbjwXPxNZjLb4Bli3DG/g5n8r+w+blulyYiIi2IRshEAM+os7HH9sVO+zd21lTs7M8wQ0djTr8Ik5DkdnkiItLMaYRMZA/Ttj2e6+7A89jLmBPTsfNn4jz4B5x/v46tqHC7PBERacYUyET+h0lIwnPlGDx/exUzdDR2+kf+xWS3bnK7NBERaaYUyETqYGLi8Fx9C56bH4BdeTiP3YEz81Os47hd2mFxvpiCXTzf7TJERKQWCmQiB2H6DcIz7nk4ti/2vVdxnhuHzdvhdlmHxFZUYKe8jfPpZLdLERGRWiiQidSDiYrFc8tDmN/eBGt/wXloDM7U97DlZW6XVj8b1kBFOWzJwO7Kd7saERH5HwpkIvVkjMEz4gw8j7wIvY/HfvwuzsM3Y3+YF/Ar/dvVP//6+y9LXaxERERqo0AmcohMfCLem8bi+dNjEBaO8/ITOE8/gM3a4nZpdbKrf4aUY6B1NCxf7HY5IiLyPxTIRA6T6XEcngcn+C9jbt2I8+xD2IKdh/QZtqIcZ8bHOK9NxBbvPip12spKWLcS0603pmc/7C9Lm+yNCSIizZUCmcgRMF4vnhFn4LnzUdhdiPPPp7BVB98T01ZV4Xz9Bc4Df8BOnoRd8BXOxD9jS4obvsiNa6GsFNO9N/TsD4W7YEtGwx9HREQOmwKZSAMwHTpjrhwDq37CfvRWne2s4+B8P9c/9+ytv0OMD8+dj+K56T7YtM5/B2cDhzK7Zrn/l649Mb36+59bvqRBjyEiIkdGgUykgXiGjsKMOBM7/SPsD/uv92U3Z+A8fg/21achOBjPzQ/gue8pzLF9Mf0H47nhHtiwxh/KShsulNnVyyGpHSYqFhMdC+06KZCJiAQYBTKRBmQuux46d8d5/bnqSf62rAzn32/gPHYH5GzHXHs7noefw/QbhDHm1/cOGILn93dDxmqc5x5pkFBmnSpY+wumW+9fj9OrP6xdgS0tOeLPFxGRhqFAJtKATFAwnhvvhZAQnJf+il38Lc64P2Kn/wczZBSeR1/yj6R5aj/1zPFD/SNl61fhPPOQf3X9Hxdit23BVh7GfpqbM6CkGLr1+vUYvfpDVSWs+vkAbxQRkcYU5HYBIs2N8cXjufEenGcfwvnH3yApBc9df/VPqq/P+48fiueGu3He/gf2g9eoXuHMeCC5PZ4x92HaJNfrs+ye0LXvCBldekJIKHb5YkzfgYfwzURE5GhRIBM5Ckz3Ppjr7oCduZhR52CCgw/t/cefiPf4E7FFBbA9E5udBdmZ2C8+wk77EHP1LfX6HLv6Z0hIwsTG/frZwcHQvY/mkYmIBBAFMpGjxDNo+BF/homMgsgoTGoPAJyCndhvv8JeeDWmddQB32sdB9b8guk/eP/P7dUf+9Mi7I5tmISkI65TRESOjOaQiTQhZtQ5UFGO/Xr6wRtnboTiIui2/6VS03PP8hfaRklEJCAokIk0ISalAxzbF/vVZ/4V+A/ArvKvP1br3LWkFPAlYLWNkohIQFAgE2liPKPPgZ252CXfHrCdXf0zxLXBxLXZ7zVjjP9uy5XL6rWzgIiIHF0KZCJNTZ80SEjCzvy0zibWWlizHNO1V51tTK/+/iUxMlYdjSpFROQQKJCJNDHG48GMOhvWrcRmrKm9UdZm/56V3eoOZPToC8ajuy1FRAKAAplIE2ROTIdWYdhZtY+S2dV71h87wNpnJiISOnfD/jDff0emiIi4RoFMpAkyYeGYE9OxC7/B7szbv8Hq5RDjg4S2B/6cEWf4R9N+/P4oVSoiIvWhQCbSRJlRZ4FThZ3zefVzdmcezpcfY39ejOnWu8ZembV+xsCToU1bnKnv+eediYiIK7QwrEgTZdokQ5807JxpOPGJ2O/nwIplYB04pgvmtAsP/hleL+bMS7FvPAfLFoG2UhIRcYVGyESaMM/oc6Bwlz9QZWdhzrwYz19ewvvgs5gOnev1GWbQcIhP1CiZiIiLGm2EbOnSpbz++us4jsPo0aM5//zza7w+depUZs6cidfrJSoqiptuuomEhITGKk+kaTq2L+a6OzBt2kLn7ge9RFkbExSEOfMS7Ft/h+WLoffxR6FQERE5kEYZIXMch0mTJnH//fczYcIE5s2bx5YtW2q06dixI48//jhPP/00gwcP5u23326M0kSaNGMMniEjMak9DiuMVX/OkJHgS8CZOlmjZCIiLmiUQLZ27VqSkpJITEwkKCiIoUOHsnDhwhptevfuTWhoKABdu3YlL6+WO8dE5KgwQcGYMy6CdSth5TKNsnOYAAAa/ElEQVS3yxERaXEa5ZJlXl4ecXFx1Y/j4uJYs6aOBS2BWbNm0a9fv1pfmzFjBjNmzADg8ccfJz4+vmGLrUVQUFCjHEcOjfqlYdnzfkPO5x/i/fxDfMNGH9FnqW8Ck/olMKlfAldj9k3A3WU5d+5c1q9fz7hx42p9PT09nfT09OrHOTk5R72m+Pj4RjmOHBr1S8Ozp1xAxXv/ZMe82QdcVPZg1DeBSf0SmNQvgauh+yY5ObnO1xrlkqXP5yM3N7f6cW5uLj6fb792y5Yt46OPPuKee+4hODi4MUoTkX2YYadAdCzO1PfcLkVEpEVplECWmppKVlYW2dnZVFZWMn/+fNLS0mq0ycjI4NVXX+Wee+4hOjq6McoSkf9hQkIxp14AK5dh165wuxwRkRajUS5Zer1errvuOsaPH4/jOIwcOZL27dszefJkUlNTSUtL4+2336a0tJRnn30W8A8T3nvvvY1Rnojswww/HTvt3ziffYD31ofdLkdEpEVotDlkAwYMYMCAATWeu+yyy6p/f+ihhxqrFBE5ABPaCnPKediP/h9241rMMV3cLklEpNnTSv0ish8z8iwIj8D57/tulyIi0iIokInIfkxYOGbUObBkAXbrRrfLERFp9hTIRKRWJv0cCA3DfvaB26WIiDR7CmQiUisT0Roz8kzswm+w27bu97rdtA7nP29hS4sP+lm2ovxolCgi0mwokIlIncwp50FwEHbav6ufs5UVOB+/i/PXu/x3Y/59/AEDl/1hPs6tl+PMm9EYJYuINEkKZCJSJxMVgxl2GnbBV9ic7djNGTjj78JOfQ9zwsmY394Eq3/G+edT2MrK/d7vfDcH559PQmUF9r/vY6uqXPgWIiKBL+C2ThKRwGJOuxA7ZxrOi+MhazNERuH544OYvicA4FiLffdl7JvPw7W3Yzz+v+c582di33geuvXGnJiOfW0CdvG3mIEnufl1REQCkgKZiByQiY3DnHQKdvY0zKDhmMtvwES0rn7dM/JMnOIi7JS3ISwCLr+B4i+m+MPYsX3xjHnAf9nzv+9jp/8Hm3YixhgXv5GISOBRIBORgzKXXo8ZdiqmQ2rtr595CRQXYb+Ygt2eSeEvS6BPGp6bxmKCQ/xtTj0f+/9ehJXL4Ni+jVm+iEjA0xwyETkoExxSZxgDMMZgLr4Wc9Ip8MsSQk8Yhuem+6rDGIAZMhKiYnC++KgxShYRaVI0QiYiDcIYA1eNwQwaTvTgk8ndubPm68EhmNHn+Ldk2pKBadfJpUpFRAKPRshEpMEYjxfT4zhMUO1/1zPDz/AvNjtdo2QiIvtSIBORRmMiIjEnn4r9fi42N7te77GlxVhHy2WISPOmS5Yi0qhM+rnYWVOxMz7BXPa7Gq/Z4iLYuA67aZ3/58Z1kJ0JSSl4/jQeE+NzqWoRkaNLgUxEGpXxJWBOOBn79RfY44dit26C9auw61fBti2/NoxrAx06Y9JOws78BOfZh/DcNR4TFeNe8SIiR4kCmYg0OnPqBdhvv8J5Yqz/idbR0KkbZvAITMeu0CEV0zqqur3t1Q/nuXG/hrLIqNo/WESkiVIgE5FGZ9p1xNxwDzhVmM7dIT7xgIvFmm698dz8IM4Lj+JMHIfnzr9gwiMbsWIRkaNLk/pFxBWegSfhGTQck5BUr5X7Tc9+eMbcB1s24Dz3CLa0uBGqFBFpHApkItJkmD5peG68BzauxZnwZ2zuDrdLEhFpEApkItKkmP6D8dxwD2zdhPOXW7E/zHO7JBGRI6ZAJiJNjhkwBM/DEyExBeflJ3DefAFbVup2WSIih02BTESaJNOmLZ57HseceQl23gycR+/ArluJtdbt0kREDpnushSRJssEBWEuuArbsx/Ov57FefweiIqB1B6Y1GMxqT3gmNQam5yLiAQiBTIRafJM9z54xj2PXfgNrFuJXbcCu2QBFiAkFDPiDMxpF2CiYt0uVUSkVgpkItIsmIjWmBFnwIgzALAF+bBuFfaHedgvP8HO/gxz8p5gts8WTLaqCnZkQX4udDlWo2ki4goFMhFplkxULPQfjOk/GHv2b7CffYCd9Sl2zjTMCSdDRTk2c5N/u6bKSv+buvfBc8tDmNBW7hYvIi2OApmINHsmKQVz3e3Ysy/1B7PvZkO0D5I7YHr1h+QOUFKCnfwvnOcfwXPLw5hWYW6XLSItiAKZiLQYpk0y5prbsFffWuvuAE7rKOykZ3GeG4fntj9jWoW7UKWItERa9kJEWpy6tmrynHAynt/fBetX4Uwchy3R9kwi0jgUyERE9mHSTvLvBLBhDc6Eh7FFBW6XJCItgAKZiMj/MMcP9YeyTetwxv4O592Xsdu2ul2WiDRjmkMmIlILM2AIngcnYL/8GPv1F9ivPoM+aXjSz4Vj+9Z52VNE5HAokImI1MG064i59jbsRf+Hnf05dvZnOBMehmgfpntv6N4b060PJCYroInIEVEgExE5CBMVizn3cuwZF2MXfQM//4Bd9RN8P9e/G0C0D9N/MOb0izBxCW6XKyJNkAKZiEg9meBgzJCRMGSkfxPz7ZnY1T/BimX+y5pff4EZdirmjIsxvni3yxWRJkSBTETkMBhjICkFk5QCJ5+Ozc32Lzr79XTsN19ghp2GOTHd37iq0r8bQFUltAqHjl10iVNEalAgExFpACauDeaqm/2XNT/7ADv3c+xX/629cZu2mKGjMUNGaSRNRAAFMhGRBmXiEzH/90fsmZfAhjUQFATe4D0/g7A7tmHnz8ROeRv78bvQsy9m8EhMag+IT9TImUgLpUAmInIUmPhEiE/c//luveDE0djsLH8w+3YWdtKz/psDwsIhpSOmfSc4pgsm7URtdC7SQiiQiYi4wLRpizn/Suy5l8PG9djN62FzBnZLBnb+LPjqv9gP38CceQlm+OmY4BC3SxaRo0iBTETERcbjhU5dMZ26Vj9nHQfWrcT55F3s5H9hv5iCOftSzNB0TJD+sy3SHOnMFhEJMMbjga498f7pMeyKH3E+fgf7/17Cfv4fzKARmN4D/CHO4631/ba0BEJC6nxdRAKPApmISAAzx/bF0+M4+PkHnM/+jf3v+9ip70FEa0zPftCzH1RVQdZmbNZmyNwMO3P9i9WemI45KR2TkOT21xCRg1AgExEJcMYY6JOGt08atqgAu+JH+HkxdvliWPi1v1FoK0hqh+lxHCQmY9evwk77N3baB3BsXzzDTsWOPtPdLyIidVIgExFpQkxkFGbgMBg4zD/XLGsLtGoFsfH+S537sHk7sN/MwM77EueVJ8l+9WmIa+MPbonJ/j04u/bCpBzj0rcRkb0UyEREmijj8UBKh7pf9yX49+A8+1L4ZSlhmRspzliL3b7VvxdneRnWeDBnXer/RzcMiLhGZ5+ISDNnPF7ofTyRI06jNCcHwL8XZ14O9uN3sFPfwy5fjOd3d2LaJLtcrUjL5Dl4ExERaW6MMZi4BDzX3Y654R7Ynonzl9txvv7CH9ZEpFFphExEpIXzDDwJm9oD5/WJ2Lf+jp0/yz/HLDwCwiP9P4OCIT8HcrOxudmQuwMKd0H7TphuvTHd+0BqD0yrMLe/jkiTpEAmIiIYXzyeO/6CnfGJf0un5UugZDeUle7TyAOxPohrg+naE8IjsRvWYKf/Bzvt3+D1+rd8GjgMM2QkJqK1e19IpIlRIBMREcB/k4A59Xw49fzq52xlBZQUQ0U5RMXWOvHflpbAupXY1T9jf17s313gwzcxxw/FDDsNuvXSpukiB6FAJiIidTJBwdA6+sBtWoVBr/6YXv3hgquwmzOwX0/HLpiD/W4OJKVgju0LbTtgkjtAcgdM66hDrsWWl2GXLPDv9ZmXjTnpFMywUzHhkYf79UQChgKZiIg0KNO+E+aKP2Avuhb7wzfY+bOwC2ZDSTHVtwu0jvavgxafCPGJ/sug8YnQOgaCgyE45NefWzb4P2Ph1/7LqHFtIDYe++83sJ9Oxgw7BTP6HP/797DFRZCTDbsLoXMPTGioG38UIvWmQCYiIkeFCQ3FDB0NQ0f779zMz4XMTdjMTf6fO7ZhVy+H7+aCdTjgvZ0hIZgBJ2KGjoLufTAeD3bjOuyXU7Bf/Rc7cyp06+UPbDnboXj3r+8NC/fPaxs6Gjp31+VTCUgKZCIictQZY8AXD754/+bo+7CVFZCXAznbsbuL/PPVKsuhosL/e+sYzIAhmLDwmp95TCrmd3/CXng1dtan/hsRYnyYzt0hPtE/YhYcgl30DXbBbOzc6dC2PWboKMwxXcCX4K8nOKQx/yhEamVsE19wJjMz86gfIz4+npw9iylK4FC/BC71TWBqyf1iS4r9wWzeDFi3suaLUTEQGw8ez68hcO8/IaEQ4/Nv1h4d6/997zIgQUH+OXZBQRDSCiKj/P+0jjqkkHck/WIrKsDr3W/bLGkYDX3OJCfXvfByo42QLV26lNdffx3HcRg9ejTnn39+jdcrKir4+9//zvr162ndujW33347bdq0aazyRESkGTNh4Zhhp8KwU7F5ObBjm389tbwdkLcDm7cDLBAVjAkJ9c9dCwqG0hLsrjz/JdYVS/13nO6jzhGN0FYQFg78z+VRY/wBzhtU/TMvLJwqjwdCQv3H3vtPRCREREFka0xklH9NuLwd2K0bsVs3QuZG2J4FwUH+/UnbtvePACZ38M+zi2wNEa39n6vLtAGvUQKZ4zhMmjSJBx98kLi4OO677z7S0tJo165ddZtZs2YRERHBCy+8wLx583jnnXe44447GqM8ERFpQczeS6eH8V5bVgqlJVBZAZWVv/4sLYHdBdiiAigsgKJCKC2u5QOcPe+rxFb5fxqDf85bwS5seRmUl/nXfyv5dR5cjeBnDCS0hZQOmONP9O9JmrUZu2Y5fDdn/5AYFOwPZhGR/pAYFuG//BseAa3CIDgUQvbeRBHqD4qYmke1ds8/Djh7fsf6g2toK0xoKwgNg9A97zde8HrA4/WPPBrPnmxq/PXv/X2/ztnzevV79vzu2fNZXm+zDZeNEsjWrl1LUlISiYn+O2CGDh3KwoULawSyRYsWcckllwAwePBgXnvtNay1zfYPXkREmh5/8GhV9+uH8ZmxdVwWs1VVUFzkD3dFBf47RmPjIKl9nXeN2pJiyNoMO3OxRYWwu8j/vt2F/jtPS4qhcBc2O8sf+EpL/Jdmj1Cjzn0yewIae4PhngIM/oC4d4QzZE/Q9Ab523u9vwbEvYFzn1lb5riBeNLPbcxvUkOjBLK8vDzi4uKqH8fFxbFmzZo623i9XsLDwyksLCQq6tDXqhEREWnqjNfrXx7kIOvA1XhPWDh07u7/vZ7vsdb6R/rKy6GizD+Pbu9gyL6DIsYDHvPryBX425aV+Ef0Svf8rKrCOg44VbDvT//B8Aehuopx/G0cu8/vez/HgaqqPY+rqB5tq/6m9te5f2VlUFHuH3F0qva8b8/opFO1z3fa5/tV7fO8C5rcXZYzZsxgxowZADz++OPEx8cf9WMGBQU1ynHk0KhfApf6JjCpXwKT+iVwNWbfNEog8/l85ObmVj/Ozc3F5/PV2iYuLo6qqiqKi4tp3Xr/fdDS09NJT0+vftwYdwy15DuTApn6JXCpbwKT+iUwqV8CV2PeZdko98mmpqaSlZVFdnY2lZWVzJ8/n7S0tBptjj/+eGbPng3AggUL6NVLe5+JiIhIy9AoI2Rer5frrruO8ePH4zgOI0eOpH379kyePJnU1FTS0tIYNWoUf//737nllluIjIzk9ttvb4zSRERERFynhWHrQcPJgUn9ErjUN4FJ/RKY1C+Bq9ldshQRERGRuimQiYiIiLhMgUxERETEZQpkIiIiIi5TIBMRERFxmQKZiIiIiMsUyERERERcpkAmIiIi4jIFMhERERGXKZCJiIiIuEyBTERERMRlCmQiIiIiLlMgExEREXGZApmIiIiIy4y11rpdhIiIiEhLphGyehg7dqzbJUgt1C+BS30TmNQvgUn9Ergas28UyERERERcpkAmIiIi4jLvuHHjxrldRFPQuXNnt0uQWqhfApf6JjCpXwKT+iVwNVbfaFK/iIiIiMt0yVJERETEZUFuFxDIli5dyuuvv47jOIwePZrzzz/f7ZJapJycHF588UV27tyJMYb09HTOPPNMioqKmDBhAjt27CAhIYE77riDyMhIt8ttkRzHYezYsfh8PsaOHUt2djYTJ06ksLCQzp07c8sttxAUpP/cNKbdu3fz8ssvs3nzZowx3HTTTSQnJ+ucCQBTp05l1qxZGGNo3749Y8aMYefOnTpnXPDSSy+xePFioqOjeeaZZwDq/H+LtZbXX3+dJUuWEBoaypgxYxr0cqZGyOrgOA6TJk3i/vvvZ8KECcybN48tW7a4XVaL5PV6ueqqq5gwYQLjx49n+vTpbNmyhSlTptCnTx+ef/55+vTpw5QpU9wutcX67LPPSElJqX789ttvc9ZZZ/HCCy8QERHBrFmzXKyuZXr99dfp168fEydO5KmnniIlJUXnTADIy8tj2rRpPP744zzzzDM4jsP8+fN1zrhkxIgR3H///TWeq+s8WbJkCdu2beP555/nhhtu4F//+leD1qJAVoe1a9eSlJREYmIiQUFBDB06lIULF7pdVosUGxtb/beQsLAwUlJSyMvLY+HChQwfPhyA4cOHq39ckpuby+LFixk9ejQA1lqWL1/O4MGDAf9/8NQ3jau4uJgVK1YwatQoAIKCgoiIiNA5EyAcx6G8vJyqqirKy8uJiYnROeOSnj177jdKXNd5smjRIk4++WSMMXTr1o3du3eTn5/fYLVoPLQOeXl5xMXFVT+Oi4tjzZo1LlYkANnZ2WRkZNClSxd27dpFbGwsADExMezatcvl6lqmN954gyuvvJKSkhIACgsLCQ8Px+v1AuDz+cjLy3OzxBYnOzubqKgoXnrpJTZu3Ejnzp255pprdM4EAJ/PxznnnMNNN91ESEgIffv2pXPnzjpnAkhd50leXh7x8fHV7eLi4sjLy6tue6Q0QiZNRmlpKc888wzXXHMN4eHhNV4zxmCMcamyluuHH34gOjpat+wHmKqqKjIyMjj11FN58sknCQ0N3e/ypM4ZdxQVFbFw4UJefPFFXnnlFUpLS1m6dKnbZUkdGvM80QhZHXw+H7m5udWPc3Nz8fl8LlbUslVWVvLMM88wbNgwBg0aBEB0dDT5+fnExsaSn59PVFSUy1W2PKtWrWLRokUsWbKE8vJySkpKeOONNyguLqaqqgqv10teXp7OnUYWFxdHXFwcXbt2BWDw4MFMmTJF50wA+Omnn2jTpk31n/2gQYNYtWqVzpkAUtd54vP5yMnJqW7X0LlAI2R1SE1NJSsri+zsbCorK5k/fz5paWlul9UiWWt5+eWXSUlJ4eyzz65+Pi0tjTlz5gAwZ84cBg4c6FaJLdYVV1zByy+/zIsvvsjtt99O7969ufXWW+nVqxcLFiwAYPbs2Tp3GllMTAxxcXFkZmYC/hDQrl07nTMBID4+njVr1lBWVoa1trpvdM4EjrrOk7S0NObOnYu1ltWrVxMeHt5glytBC8Me0OLFi3nzzTdxHIeRI0dy4YUXul1Si7Ry5UoefvhhOnToUD10fPnll9O1a1cmTJhATk6ObuEPAMuXL+fTTz9l7NixbN++nYkTJ1JUVESnTp245ZZbCA4OdrvEFmXDhg28/PLLVFZW0qZNG8aMGYO1VudMAHj//feZP38+Xq+Xjh078oc//IG8vDydMy6YOHEiv/zyC4WFhURHR3PppZcycODAWs8Tay2TJk3ixx9/JCQkhDFjxpCamtpgtSiQiYiIiLhMlyxFREREXKZAJiIiIuIyBTIRERERlymQiYiIiLhMgUxERETEZQpkIiL1cOmll7Jt2za3yxCRZkor9YtIk3PzzTezc+dOPJ5f/045YsQIrr/+eherqt306dPJzc3liiuu4M9//jPXXXcdxxxzjNtliUiAUSATkSbp3nvv5bjjjnO7jINav349AwYMwHEctm7dSrt27dwuSUQCkAKZiDQrs2fPZubMmXTs2JG5c+cSGxvL9ddfT58+fQDIy8vj1VdfZeXKlURGRnLeeeeRnp4OgOM4TJkyha+++opdu3bRtm1b7r77buLj4wFYtmwZf/3rXykoKOCkk07i+uuvP+jGw+vXr+fiiy8mMzOThIQEvF7v0f0DEJEmSYFMRJqdNWvWMGjQICZNmsT333/P008/zYsvvkhkZCTPPfcc7du355VXXiEzM5NHH32UpKQkevfuzdSpU5k3bx733Xcfbdu2ZePGjYSGhlZ/7uLFi/nb3/5GSUkJ9957L2lpafTr12+/41dUVPD73/8eay2lpaXcfffdVFZW4jgO11xzDeeee662YhORGhTIRKRJeuqpp2qMNl155ZXVI13R0dGcddZZGGMYOnQon376KYsXL6Znz56sXLmSsWPHEhISQseOHRk9ejRz5syhd+/ezJw5kyuvvJLk5GQAOnbsWOOY559/PhEREURERNCrVy82bNhQayALDg7mjTfeYObMmWzevJlrrrmGxx57jN/85jd06dLl6P2hiEiTpUAmIk3S3XffXeccMp/PV+NSYkJCAnl5eeTn5xMZGUlYWFj1a/Hx8axbtw6A3NxcEhMT6zxmTExM9e+hoaGUlpbW2m7ixIksXbqUsrIygoOD+eqrrygtLWXt2rW0bduWv/3tb4f0XUWk+VMgE5FmJy8vD2ttdSjLyckhLS2N2NhYioqKKCkpqQ5lOTk5+Hw+AOLi4ti+fTsdOnQ4ouPffvvtOI7DDTfcwD//+U9++OEHvv32W2699dYj+2Ii0mxpHTIRaXZ27drFtGnTqKys5Ntvv2Xr1q3079+f+Ph4unfvzrvvvkt5eTkbN27kq6++YtiwYQCMHj2ayZMnk5WVhbWWjRs3UlhYeFg1bN26lcTERDweDxkZGaSmpjbkVxSRZkYjZCLSJD3xxBM11iE77rjjuPvuuwHo2rUrWVlZXH/99cTExHDnnXfSunVrAG677TZeffVVbrzxRiIjI7nkkkuqL32effbZVFRU8Nhjj1FYWEhKSgp33XXXYdW3fv16OnXqVP37eeeddyRfV0SaOWOttW4XISLSUPYue/Hoo4+6XYqISL3pkqWIiIiIyxTIRERERFymS5YiIiIiLtMImYiIiIjLFMhEREREXKZAJiIiIuIyBTIRERERlymQiYiIiLhMgUxERETEZf8/OmuOdytmGiwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fixing Overfitting\n"
      ],
      "metadata": {
        "id": "nGXxSFYN0xM8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are many techniques that we can employ to fix the problem of verfitting.\n",
        "Below are the most common and recommended ones:\n",
        "\n",
        "\n",
        "1. Simplify the complexity of the model  \n",
        "2. L2 Regularization\n",
        "2.   Drop Out\n",
        "3. Early Stopping\n",
        "4. Data Augmentation\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "qTpamFlY294d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. **Simplify the complexity of the model**\n",
        "\n",
        "The first step when dealing with overfitting is to decrease the complexity of the model. To decrease the complexity, we can simply remove layers or reduce the number of neurons to make the network smaller. While doing this, it is important to calculate the input and output dimensions of the various layers involved in the neural network. There is no general rule on how much to remove or how large your network should be. But, if your neural network is overfitting, try making it smaller.\n",
        "\n"
      ],
      "metadata": {
        "id": "n1p0tUZC4t7k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. **L2 Regularization**\n",
        "\n",
        "L2 regularization introduces a new cost function by adding a penalty term to a loss function called an L2 weight penalty.\n",
        "\n",
        "It is also known as weight decay as it pushes the values of the weights towards zero (but not zero).\n"
      ],
      "metadata": {
        "id": "GTX-QdF65TXV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "gMFVMY7aCpWl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Drop Out**"
      ],
      "metadata": {
        "id": "nRnc6OU5P8xh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Regularization techniques prevent the model from overfitting by modifying the cost function. Dropout, on the other hand, prevents overfitting by modifying the network itself. It works as follows. Every neuron apart from the ones in the output layer is assigned a probability p of being temporarily ignored from calculations. p is also called dropout rate and is usually initialized to 0.5. Then, as each iteration progresses, the neurons in each layer with the highest probability get dropped. This results in creating a smaller network with each epoch. Since in each iteration, a random input value can be eliminated, the network tries to balance the risk and not to favour any of the features and reduces bias and noise. "
      ],
      "metadata": {
        "id": "bJBjeNwGR89T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "sC1wF3SvRMQl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "ni-EyEWRRgHM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br>\n",
        "\n",
        "\n",
        "# Adding L2 Regularization from Keras"
      ],
      "metadata": {
        "id": "xSY3v0FSC0vO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#kernel_regularizer=tf.keras.regularizers.l2(0.1)\n",
        "\n",
        "\n",
        "\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(80, input_dim=60, activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.1)),\n",
        "    keras.layers.Dense(30, activation='relu'),\n",
        "    keras.layers.Dense(15, activation='relu'),\n",
        "    keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "H = model.fit(X_train, y_train, epochs=100, batch_size=8, validation_split=0.3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "You4UFDb0wgB",
        "outputId": "cafcc8c2-70af-42fd-b3de-6bdff23cf859"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "14/14 [==============================] - 2s 43ms/step - loss: 6.8815 - accuracy: 0.5229 - val_loss: 6.2077 - val_accuracy: 0.5319\n",
            "Epoch 2/100\n",
            "14/14 [==============================] - 0s 19ms/step - loss: 5.6944 - accuracy: 0.5229 - val_loss: 5.1309 - val_accuracy: 0.5319\n",
            "Epoch 3/100\n",
            "14/14 [==============================] - 0s 17ms/step - loss: 4.7073 - accuracy: 0.6330 - val_loss: 4.2347 - val_accuracy: 0.5957\n",
            "Epoch 4/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 3.8926 - accuracy: 0.6330 - val_loss: 3.5085 - val_accuracy: 0.7234\n",
            "Epoch 5/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 3.2312 - accuracy: 0.6330 - val_loss: 2.9187 - val_accuracy: 0.6383\n",
            "Epoch 6/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 2.6942 - accuracy: 0.6789 - val_loss: 2.4400 - val_accuracy: 0.7234\n",
            "Epoch 7/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 2.2618 - accuracy: 0.6789 - val_loss: 2.0581 - val_accuracy: 0.6170\n",
            "Epoch 8/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 1.9147 - accuracy: 0.6972 - val_loss: 1.7510 - val_accuracy: 0.7447\n",
            "Epoch 9/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 1.6355 - accuracy: 0.6697 - val_loss: 1.5049 - val_accuracy: 0.7021\n",
            "Epoch 10/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 1.4156 - accuracy: 0.6972 - val_loss: 1.3079 - val_accuracy: 0.7660\n",
            "Epoch 11/100\n",
            "14/14 [==============================] - 0s 7ms/step - loss: 1.2441 - accuracy: 0.6789 - val_loss: 1.1555 - val_accuracy: 0.6383\n",
            "Epoch 12/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 1.0985 - accuracy: 0.6881 - val_loss: 1.0306 - val_accuracy: 0.7872\n",
            "Epoch 13/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.9873 - accuracy: 0.7339 - val_loss: 0.9525 - val_accuracy: 0.5745\n",
            "Epoch 14/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.9127 - accuracy: 0.6789 - val_loss: 0.8570 - val_accuracy: 0.7872\n",
            "Epoch 15/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.8284 - accuracy: 0.7064 - val_loss: 0.7930 - val_accuracy: 0.7447\n",
            "Epoch 16/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.8228 - accuracy: 0.6606 - val_loss: 0.7614 - val_accuracy: 0.7234\n",
            "Epoch 17/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.7390 - accuracy: 0.7156 - val_loss: 0.7060 - val_accuracy: 0.7660\n",
            "Epoch 18/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.6854 - accuracy: 0.8073 - val_loss: 0.6687 - val_accuracy: 0.7872\n",
            "Epoch 19/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.6493 - accuracy: 0.8073 - val_loss: 0.6335 - val_accuracy: 0.8298\n",
            "Epoch 20/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.6132 - accuracy: 0.7890 - val_loss: 0.6230 - val_accuracy: 0.7234\n",
            "Epoch 21/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.6291 - accuracy: 0.6881 - val_loss: 0.5946 - val_accuracy: 0.7660\n",
            "Epoch 22/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.5937 - accuracy: 0.7982 - val_loss: 0.5744 - val_accuracy: 0.7872\n",
            "Epoch 23/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5844 - accuracy: 0.7523 - val_loss: 0.5632 - val_accuracy: 0.8298\n",
            "Epoch 24/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.5525 - accuracy: 0.7890 - val_loss: 0.5521 - val_accuracy: 0.8298\n",
            "Epoch 25/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.5573 - accuracy: 0.7615 - val_loss: 0.5489 - val_accuracy: 0.8085\n",
            "Epoch 26/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.5286 - accuracy: 0.7890 - val_loss: 0.5391 - val_accuracy: 0.7872\n",
            "Epoch 27/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5126 - accuracy: 0.7982 - val_loss: 0.5575 - val_accuracy: 0.8298\n",
            "Epoch 28/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.4852 - accuracy: 0.8807 - val_loss: 0.5382 - val_accuracy: 0.8298\n",
            "Epoch 29/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5677 - accuracy: 0.7431 - val_loss: 0.6385 - val_accuracy: 0.7872\n",
            "Epoch 30/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.5339 - accuracy: 0.7706 - val_loss: 0.5796 - val_accuracy: 0.8085\n",
            "Epoch 31/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4779 - accuracy: 0.8257 - val_loss: 0.5591 - val_accuracy: 0.8298\n",
            "Epoch 32/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.5274 - accuracy: 0.7615 - val_loss: 0.7703 - val_accuracy: 0.6596\n",
            "Epoch 33/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.8167 - accuracy: 0.5780 - val_loss: 0.5490 - val_accuracy: 0.8298\n",
            "Epoch 34/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5796 - accuracy: 0.7798 - val_loss: 0.5484 - val_accuracy: 0.8511\n",
            "Epoch 35/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5624 - accuracy: 0.7706 - val_loss: 0.6083 - val_accuracy: 0.7660\n",
            "Epoch 36/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4708 - accuracy: 0.8899 - val_loss: 0.5305 - val_accuracy: 0.8298\n",
            "Epoch 37/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5020 - accuracy: 0.8165 - val_loss: 0.5226 - val_accuracy: 0.8298\n",
            "Epoch 38/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5708 - accuracy: 0.7248 - val_loss: 0.5791 - val_accuracy: 0.6809\n",
            "Epoch 39/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5705 - accuracy: 0.7706 - val_loss: 0.5289 - val_accuracy: 0.7872\n",
            "Epoch 40/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5420 - accuracy: 0.7615 - val_loss: 0.8229 - val_accuracy: 0.5745\n",
            "Epoch 41/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.5898 - accuracy: 0.6972 - val_loss: 0.5385 - val_accuracy: 0.8298\n",
            "Epoch 42/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4569 - accuracy: 0.8716 - val_loss: 0.5428 - val_accuracy: 0.8298\n",
            "Epoch 43/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4439 - accuracy: 0.8991 - val_loss: 0.5688 - val_accuracy: 0.8298\n",
            "Epoch 44/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.4342 - accuracy: 0.8991 - val_loss: 0.5312 - val_accuracy: 0.8298\n",
            "Epoch 45/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4108 - accuracy: 0.9266 - val_loss: 0.5909 - val_accuracy: 0.8085\n",
            "Epoch 46/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4455 - accuracy: 0.8257 - val_loss: 0.5245 - val_accuracy: 0.8511\n",
            "Epoch 47/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4100 - accuracy: 0.8991 - val_loss: 0.5337 - val_accuracy: 0.8298\n",
            "Epoch 48/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4441 - accuracy: 0.8532 - val_loss: 0.5793 - val_accuracy: 0.7234\n",
            "Epoch 49/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.5292 - accuracy: 0.7798 - val_loss: 0.5169 - val_accuracy: 0.8298\n",
            "Epoch 50/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4500 - accuracy: 0.8440 - val_loss: 0.5314 - val_accuracy: 0.8298\n",
            "Epoch 51/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.4066 - accuracy: 0.8807 - val_loss: 0.5309 - val_accuracy: 0.8511\n",
            "Epoch 52/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.4166 - accuracy: 0.8716 - val_loss: 0.5175 - val_accuracy: 0.8511\n",
            "Epoch 53/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4179 - accuracy: 0.8716 - val_loss: 0.5367 - val_accuracy: 0.8298\n",
            "Epoch 54/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4042 - accuracy: 0.8716 - val_loss: 0.5330 - val_accuracy: 0.8511\n",
            "Epoch 55/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4167 - accuracy: 0.8624 - val_loss: 0.5612 - val_accuracy: 0.8298\n",
            "Epoch 56/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3667 - accuracy: 0.9174 - val_loss: 0.5392 - val_accuracy: 0.8511\n",
            "Epoch 57/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.4079 - accuracy: 0.8624 - val_loss: 0.6222 - val_accuracy: 0.8298\n",
            "Epoch 58/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3739 - accuracy: 0.8991 - val_loss: 0.5359 - val_accuracy: 0.8298\n",
            "Epoch 59/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3454 - accuracy: 0.9358 - val_loss: 0.5271 - val_accuracy: 0.8511\n",
            "Epoch 60/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3584 - accuracy: 0.9083 - val_loss: 0.5395 - val_accuracy: 0.8723\n",
            "Epoch 61/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3469 - accuracy: 0.9358 - val_loss: 0.5404 - val_accuracy: 0.8298\n",
            "Epoch 62/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.3790 - accuracy: 0.8991 - val_loss: 0.5437 - val_accuracy: 0.8511\n",
            "Epoch 63/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.3409 - accuracy: 0.9174 - val_loss: 0.5354 - val_accuracy: 0.8511\n",
            "Epoch 64/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3209 - accuracy: 0.9358 - val_loss: 0.5491 - val_accuracy: 0.8511\n",
            "Epoch 65/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3252 - accuracy: 0.9450 - val_loss: 0.5737 - val_accuracy: 0.8298\n",
            "Epoch 66/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.3532 - accuracy: 0.9083 - val_loss: 0.5632 - val_accuracy: 0.8511\n",
            "Epoch 67/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3209 - accuracy: 0.9450 - val_loss: 0.5269 - val_accuracy: 0.8511\n",
            "Epoch 68/100\n",
            "14/14 [==============================] - 0s 7ms/step - loss: 0.3550 - accuracy: 0.8624 - val_loss: 0.5915 - val_accuracy: 0.8298\n",
            "Epoch 69/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3661 - accuracy: 0.8807 - val_loss: 0.6442 - val_accuracy: 0.8298\n",
            "Epoch 70/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.3220 - accuracy: 0.9541 - val_loss: 0.5495 - val_accuracy: 0.8511\n",
            "Epoch 71/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3018 - accuracy: 0.9541 - val_loss: 0.5905 - val_accuracy: 0.8298\n",
            "Epoch 72/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3286 - accuracy: 0.8991 - val_loss: 0.5790 - val_accuracy: 0.8298\n",
            "Epoch 73/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3462 - accuracy: 0.8716 - val_loss: 0.5980 - val_accuracy: 0.8298\n",
            "Epoch 74/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3792 - accuracy: 0.8532 - val_loss: 0.6524 - val_accuracy: 0.8085\n",
            "Epoch 75/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.3205 - accuracy: 0.9083 - val_loss: 0.5744 - val_accuracy: 0.8511\n",
            "Epoch 76/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.2823 - accuracy: 0.9450 - val_loss: 0.6262 - val_accuracy: 0.8085\n",
            "Epoch 77/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.2779 - accuracy: 0.9541 - val_loss: 0.6110 - val_accuracy: 0.8298\n",
            "Epoch 78/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.2835 - accuracy: 0.9450 - val_loss: 0.5836 - val_accuracy: 0.8511\n",
            "Epoch 79/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2812 - accuracy: 0.9358 - val_loss: 0.6020 - val_accuracy: 0.8298\n",
            "Epoch 80/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4125 - accuracy: 0.8257 - val_loss: 0.6359 - val_accuracy: 0.7660\n",
            "Epoch 81/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4133 - accuracy: 0.8440 - val_loss: 0.5313 - val_accuracy: 0.8298\n",
            "Epoch 82/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3191 - accuracy: 0.9174 - val_loss: 0.5584 - val_accuracy: 0.8511\n",
            "Epoch 83/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2831 - accuracy: 0.9633 - val_loss: 0.6095 - val_accuracy: 0.8511\n",
            "Epoch 84/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2535 - accuracy: 0.9817 - val_loss: 0.6545 - val_accuracy: 0.8298\n",
            "Epoch 85/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2899 - accuracy: 0.9174 - val_loss: 0.7245 - val_accuracy: 0.8085\n",
            "Epoch 86/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.2962 - accuracy: 0.9541 - val_loss: 0.6020 - val_accuracy: 0.8511\n",
            "Epoch 87/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.2843 - accuracy: 0.9266 - val_loss: 0.6124 - val_accuracy: 0.8298\n",
            "Epoch 88/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.3301 - accuracy: 0.8899 - val_loss: 0.5972 - val_accuracy: 0.8723\n",
            "Epoch 89/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2283 - accuracy: 0.9541 - val_loss: 0.6626 - val_accuracy: 0.8511\n",
            "Epoch 90/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2410 - accuracy: 0.9817 - val_loss: 0.6524 - val_accuracy: 0.8511\n",
            "Epoch 91/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2986 - accuracy: 0.9083 - val_loss: 0.6553 - val_accuracy: 0.8511\n",
            "Epoch 92/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2767 - accuracy: 0.9266 - val_loss: 0.6305 - val_accuracy: 0.8298\n",
            "Epoch 93/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2607 - accuracy: 0.9358 - val_loss: 0.6475 - val_accuracy: 0.8511\n",
            "Epoch 94/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2717 - accuracy: 0.9358 - val_loss: 0.6188 - val_accuracy: 0.8511\n",
            "Epoch 95/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2314 - accuracy: 0.9817 - val_loss: 0.6974 - val_accuracy: 0.8511\n",
            "Epoch 96/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2158 - accuracy: 0.9817 - val_loss: 0.6626 - val_accuracy: 0.8298\n",
            "Epoch 97/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3150 - accuracy: 0.8807 - val_loss: 0.6630 - val_accuracy: 0.8511\n",
            "Epoch 98/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3211 - accuracy: 0.9174 - val_loss: 0.6204 - val_accuracy: 0.8511\n",
            "Epoch 99/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2307 - accuracy: 0.9450 - val_loss: 0.6575 - val_accuracy: 0.8298\n",
            "Epoch 100/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2830 - accuracy: 0.9266 - val_loss: 0.6836 - val_accuracy: 0.8511\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, acc = model.evaluate(X_test, y_test)\n",
        "print('Test Accuracy: ', acc*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Br5MgBy0wct",
        "outputId": "25d96407-cf8e-4468-d1c7-6ae2bb2f08f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 6ms/step - loss: 0.7239 - accuracy: 0.8269\n",
            "Test Accuracy:  82.69230723381042\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, acc = model.evaluate(X_train, y_train)\n",
        "print('Test Accuracy: ', acc*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IundgYv8Ok-T",
        "outputId": "94350eb7-6357-457f-c98f-cface3ebed19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 0s 3ms/step - loss: 0.3594 - accuracy: 0.9295\n",
            "Test Accuracy:  92.94871687889099\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "epochs = np.arange(0, len(H.history[\"loss\"]))\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.style.use(\"ggplot\")\n",
        "\n",
        "\n",
        "# plot training and validation loss\n",
        "plt.style.use(\"ggplot\")\n",
        "plt.plot(epochs, H.history[\"loss\"], label=\"train_loss\")\n",
        "plt.plot(epochs, H.history[\"val_loss\"], label=\"val_loss\")\n",
        "plt.title(\"Training Vs Validation Loss\")\n",
        "plt.xlabel(\"Epoch #\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "TkSwcPDf0wNe",
        "outputId": "9a4a121a-2d25-4d54-bdb9-0203e6c8c63e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5e7849fd10>"
            ]
          },
          "metadata": {},
          "execution_count": 186
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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eeecdXXjhhSopKVEgENBFF12k888/X5J0yCGHaNGiRaqrq9P555+vqVOn6sMPP1ReXp6efPLJLn3z7+2339Ytt9yiUCik/fffX3fccYdcLpduv/12vfbaa3K5XJo2bZpuvvlmvfTSS5o3b54cDofS09O1cOHCHn82MQ1awWBQH330kc4999wOf15cXKzi4mJJUlFRkfx+f1TrcblcUR8D3ce8xC/mJj4xL/ErXuZmy5Ytbd86DDocCvdyc7zD4djttxp//etfa9WqVVq8eLHeffddnXfeeXrrrbc0bNgwSdL999+vrKwsNTQ0aMaMGTrllFOUnZ0ty7LkdDrldDq1du1aPfroo5o3b54uueQS/fOf/9QZZ5zRaT1Op1PBYFDXXHON/va3v2nUqFH67//+bz3zzDM688wz9eqrr+rdd9+VZVmqqqqSy+XSfffdpz//+c8aNGhQ27WOJCcnd3leYxq0li1bphEjRigzM7PDnxcWFqqwsLDtcbRvxMnNPuMT8xK/mJv4xLzEr3iZm0AgsOPcqLMuikrf0O62AUOhUNtzQqGQJk+erMGDB7e95rHHHtOiRYskRb5A99VXX+nAAw+UMUahUEihUEhDhgzR+PHjFQwGNXHiRK1bt67TMcPhsEKhkFatWqUhQ4Zo2LBhCgaDOv300/WHP/xBF110kZKSknTVVVe1ZY9gMKiDDjpIV155pWbOnKkTTzyx0/cPBAK7zGtc3FSabUMAAJCSktL293//+996++239dJLL6m4uFgTJ07s8NT25OTktr87nc628LY3XC6XXnnlFZ100kkqLi7WeeedJ0m68847df3116ukpEQnnniiysvL93qMVjELWo2NjVq+fLkOOeSQWA0JAADigM/nU21tbYc/q6mpUUZGhrxer9asWaOPP/6418YdNWqUNmzYoLVrIz1pCxYs0KGHHqq6ujrV1NRo+vTp+s1vfqOVK1dKktatW6cpU6bouuuu04ABA1RSUtLjGmK2dejxePTkk0/GajgAABAnsrOzdfDBB+vYY4+Vx+Np19909NFHa/78+TrqqKM0atQoTZkypdfG9Xg8uvfee3XZZZe1NcNfcMEFqq2t1axZsxQIBGSM0Zw5cyRJt956q9auXStjjI488khNmDChxzVYxhjT43eJkt5Ikp0xTQENyMhQeUNj1MbA3omXngbsirmJT8xL/IqXuamvr2+3XZfoXC7XHo+W2J2OPs+46NGKJ+HbrlX172+3uwwAANCPJe4dJn2pCtd0/7RZAAAQH2688UZ98MEH7a5dfPHFOvvss22qaFcJHLTSZSq22V0FAADYS7ffHv87Uwm7dWilprGiBQAAoiphg1Zk67BacfxdAAAA0MclcNBKl5qbpKYmuysBAAD9VAIHrdTIP+uq7a0DAAD0WwkbtKzUtMhfamvsLQQAALQzZsyYTn+2YcMGHXvssTGspmcSNmjJlx75Zx1BCwAAREcCH+/QunVI0AIAJI7//XCL1lb07l1RRmR5dPFBAzv9+e233678/HzNnj1bkjR37lw5nU79+9//VlVVlYLBoK6//nrNmDGjW+M2Njbqhhtu0PLly+V0OjVnzhwdccQRWrVqla655ho1NTXJGKPHHntMeXl5uuyyy1RaWqpwOKyrrrpKP/jBD3rya3dJ4gatlq1DU1cry+ZSAADoz0455RTNmTOnLWi99NJLevbZZ3XRRRcpLS1N5eXlmjlzpo4//nhZVtf/rfz000/Lsiy98cYbWrNmjX74wx/q7bff1vz583XRRRfptNNOU1NTk0KhkN58803l5eVp/vz5crlcKi8vj9Jv217iBi1fa48WzfAAgMSxu5WnaJk4caLKysq0efNmbd++XRkZGcrNzdVvfvMbvf/++7IsS5s3b9a2bduUm5vb5ff94IMP9OMf/1iSNHr0aBUUFOibb77RgQceqAceeEClpaU68cQTNXLkSI0fP17/7//9P912222aMWOGDjrooGj9uu0kbI+W5U6Skj1sHQIAEAMnn3yyXnnlFb344os65ZRTtHDhQm3fvl2LFi3S66+/Lr/fr0Ag0CtjnXrqqXrqqafk8Xh0wQUX6J133tGoUaP06quvavz48SoqKtK8efN6Zaw9SdigJUmOtAyprtbuMgAA6PdOOeUUvfDCC3rllVd08sknq6amRn6/X263W++++642btzY7fecOnWq/v73v0uSvv76a23atEmjRo3S+vXrNWzYMF100UWaMWOGvvjiC23evFler1enn366fvrTn+qzzz7r7V+xQ4m7dSjJkZauICtaAABE3bhx41RXV6e8vDwNHDhQp512mmbNmqXp06dr0qRJGj16dLffc9asWbrhhhs0ffp0OZ1OzZs3T8nJyXrppZe0YMECuVwu5ebm6sorr9Snn36qW2+9VZZlKSkpKWb3SbRMHN+DpqSkJKrv73zgt2qqr5Pzl3dFdRx0j9/vV1lZmd1loAPMTXxiXuJXvMxNfX29UlJS7C4jbrhcLgWDwb1+fUefZ35+fofPTeitQ4utQwAAEEWJvXWYmk4zPAAAceiLL77Qz372s3bXkpOT9fLLL9tU0d5J7KCVFglaxphundsBAACia5999tHrr79udxk9xtZhOCw11NtdCgAAURPH7dh9Unc+z4QOWo407ncIAOj/HA5Hj5q/sUMwGJTD0fX4lOBbhxmRv9TVSDl59hYDAECUeDweNTY2KhAI0CqjSK/X3hyOaoyRw+GQx+Pp8msSOmhZrUGrlhUtAED/ZVmWvF6v3WXEjVgeu5HYW4dtN5YmaAEAgN6X2EFr561DAACAXpbQQctqWdFi6xAAAERDYgctp0vy+qR6TocHAAC9L6GDliQpNU2qrba7CgAA0A8RtFJSaYYHAABRQdBKTePG0gAAICoSPmhZvnS2DgEAQFQkfNCSL5XjHQAAQFQQtFLTpPo6mXDI7koAAEA/Q9Dytd5Yus7eOgAAQL9D0PKlRv7J9iEAAOhlCR+02k6HJ2gBAIBelvBBSz5uwwMAAKKDoNUStDi0FAAA9DaCFluHAAAgSlx2F2CX2kBIzvomyZMiWQ62DgEAQK9L2BWtG15fr7vf/FqWwxH55mE9QQsAAPSuhA1a6R6XKhuaIw9S01jRAgAAvS5xg1ayU1WNLUHLl0YzPAAA6HUJG7Qykp07VrR8aTTDAwCAXpewQSst2anqxqBCYSPLl8rWIQAA6HUJG7QyPE6FjVTXFIrc77Cu1u6SAABAP5OwQSs9OXKyRXUgFGmGDzTIBJttrgoAAPQnCRy0nJKkqkBox42l2T4EAAC9KGGDVoYnErSqG1u2DiW2DwEAQK+K2cnwdXV1euSRR7RhwwZZlqWf/OQnGjt2bKyG30XrilZ1ICQrNU1GkuqqbasHAAD0PzELWk899ZQmT56sa6+9VsFgUIFAIFZDd2jH1mFQSm/ZOmRFCwAA9KKYbB3W19friy++0LHHHitJcrlc8vl8sRi6U26nQylJzkgzfMvWoallRQsAAPSemKxobd26Venp6XrooYe0fv16jRw5UrNnz5bH44nF8J3K9LojPVqpGZELHFoKAAB6UUyCVigU0tq1a3XhhRdqzJgxeuqpp/T888/rnHPOafe84uJiFRcXS5KKiork9/ujWleWd5MajUP+/MHa6nLLGw4pLcpjYs9cLlfU5x57h7mJT8xL/GJu4lMs5yUmQWvAgAEaMGCAxowZI0k69NBD9fzzz+/yvMLCQhUWFrY9Lisri2pdGR6XtlTXa/v27ZIvTQ1lWxWI8pjYM7/fH/W5x95hbuIT8xK/mJv4FI15yc/P7/B6THq0MjMzNWDAAJWUlEiSPvvsMxUUFMRi6N3KaN06lCRfKjeWBgAAvSpm3zq88MIL9cADDygYDCo3N1c//elPYzV0pzK97siBpVLkdHgOLAUAAL0oZkFr+PDhKioqitVwXZLpdaspZBQIhuXypUlbS+0uCQAA9CMJezK8JGV6IzmzqjEky5fGtw4BAECvSuigleF1S2o5tNQX2To0xthcFQAA6C8SOmhltQStmkAo0qMVbJaa7D2xHgAA9B8JHbQyPC0rWo2hyIqWxPYhAADoNQkdtDJTIkGrOtDSoyXxzUMAANBrEjpopSY55bQiQUuprGgBAIDeldBBy7IspSc7Vd3aDC8RtAAAQK9J6KAlSekeV7seLcPWIQAA6CUErWRn5FuHrGgBAIBeRtBKdqoqEJLldkvJHoIWAADoNQStZGekGV5qO7QUAACgNyR80MrwOFUbCCkUNpIvVaa+1u6SAABAP5HwQSs92SUjqbYpJKWmS7XVdpcEAAD6CYJWslOSIn1aqelSDUELAAD0DoKWJxK0qhtDUlqGVFNlc0UAAKC/SPigldGyolUdCErpmVJDnUxzs81VAQCA/iDhg1Za69Zh64qWxKoWAADoFQkftNKTXZKkmkBIVnpm5GJNpY0VAQCA/iLhg5bbacnndqgqsNOKVjUrWgAAoOcSPmhJke3D6kAo0qMlyVSzogUAAHqOoKXIoaXVjcG2oMXWIQAA6A0ELe24DY+V7JGSkmmGBwAAvYKgpUhDfFXr/Q7TMyW2DgEAQC8gaCmyolUTCMkYI6VlyNAMDwAAegFBS5HT4ZtCRo1Bw4oWAADoNQQt7bjfYXUgGDlLix4tAADQCwhakjJaDi2tbj1Lq6ZSJhy2uSoAANDXEbS048bSVY0tZ2mFw1J9rc1VAQCAvo6gpZ23DrnfIQAA6D0ELXXQoyXREA8AAHqMoCUpxe2QyyFVN4aktNbb8LCiBQAAeoagJcmyrB2Hlqa33liaFS0AANAzBK0WrYeWypcmWQ7udwgAAHqMoNUi3eNUVWNIlsMhpaXTDA8AAHqMoNWi9cbSkQeZMmwdAgCAHiJotchIdqo6EIw8SMtgRQsAAPQYQatFerJLtU1hhcJGVhr3OwQAAD1H0GrRejp8TaDldHiOdwAAAD1E0GrR7nT49Awp0CATCNhcFQAA6MsIWi1ag1ZVIBhZ0ZI44gEAAPQIQatF24pWY0gW9zsEAAC9gKDVIsPjktR6Y2nudwgAAHqOoNUirW3rMNS2dchZWgAAoCcIWi1cDku+JMeOZniJFS0AANAjBK2dpCc7Vd0YlOVOkrwp9GgBAIAeIWjtJD3ZteM2PJwODwAAeoigtZOMlhtLS5LSMujRAgAAPULQ2kmWx6WKxpb7HaZnsqIFAAB6hKC1k0yvU9WNIe53CAAAegVBaydZHpeMpMrGltPha6tlwiG7ywIAAH0UQWsn2d7IoaUVDS1HPBgj1dbYXBUAAOirXLEa6IorrpDH45HD4ZDT6VRRUVGshu6yrLagFZSVliEjRbYPW+99CAAA0A0xC1qSNGfOHKWnp8dyyG5pC1qNO99YmoZ4AACwd9g63EmmZ8eKVuv9DjniAQAA7K2YrmjddtttkqTjjjtOhYWFu/y8uLhYxcXFkqSioiL5/f6o1uNyuXYZI8PztRqMSwNGjNQ2Sb5wUL4o14H2OpoXxAfmJj4xL/GLuYlPsZyXmAWtW265RdnZ2aqqqtKtt96q/Px87bvvvu2eU1hY2C6AlZWVRbUmv9+/yxgZyQ6VVtZqe0OG5HSqrnSjGqJcB9rraF4QH5ib+MS8xC/mJj5FY17y8/M7vB6zrcPs7GxJUkZGhg4++GCtWbMmVkN3S5bXFWmGt6zIbXiq6dECAAB7JyZBq7GxUQ0NDW1/X758uYYOHRqLobutNWhJktIzZWiGBwAAeykmW4dVVVW65557JEmhUEhHHnmkJk+eHIuhuy3L41JFQ0jGmJYVLZrhAQDA3olJ0Bo4cKDuvvvuWAzVY1lel5rDRnVNYaWkZ8ps3mR3SQAAoI/ieIfvaD1Lq7yx5YiH6srI6hYAAEA3EbS+I8vrlCRVNrQcWtrcJAUabK4KAAD0RQSt72hb0WoIRnq0JL55CAAA9gpB6zuyd77fYXpr0KIhHgAAdB9B6zu8LoeSnFbkiAfudwgAAHqAoPUdlmUp2+tSRWOI+x0CAIAeIWh1oO3Q0rT0yIUaghYAAOg+glYHMj0tt+FxuaWUVHq0AADAXiFodSDb61RFY+tteLjfIQAA2DsErQ5keV2qaworEAxzv5vZbBkAACAASURBVEMAALDXCFodaD1Lq7IxyP0OAQDAXiNodSDL03qWVkhWeiZBCwAA7BWCVgeydjq0VBnZUn2tTFPA5qoAAEBfQ9DqQLvb8GQNiFysLLexIgAA0BcRtDqQnuyUw4r0aFmZLUGrYru9RQEAgD6HoNUBp8NShsfVbkXLVBK0AABA9xC0OpHlcUZ6tFpXtAhaAACgmwhanWi9DY/lTZGSvWwdAgCAbiNodSKr9cbSkpSVzdYhAADoNoJWJ7I8LlU1BhUKm8j2Id86BAAA3UTQ6kSW16WwkaoDIVlZA9g6BAAA3UbQ6kT2zoeWZg6QqsplwmGbqwIAAH0JQasTmV6npJaglTVACoUkbi4NAAC6gaDVibYVrZ0PLaUhHgAAdANBqxNZ3906lOjTAgAA3ULQ6kSS0yFfkqNl6zBbEqfDAwCA7iFo7UaWx6XyhpCUnik5HFIFRzwAAICuI2jtRpbXFbmxtMMpZWTTowUAALqFoLUbrbfhkSRlZstUlNlbEAAA6FMIWruR5XGqvCEoY0zkiAdOhwcAAN1A0NqNLK9LTSGj+uZw5IgHtg4BAEA3ELR2I2uns7SUOUBqqJdpbLC5KgAA0FcQtHaj3VlaLUc8sKoFAAC6iqC1GzuCVkhWlj9ykUNLAQBAFxG0diPbs+vp8IaGeAAA0EUErd3wJTnkdljtb8PD1iEAAOgigtZuWJalLK9TFQ1BWcnJUopP4iwtAADQRQStPcjyuiLfOpSkzAEy3IYHAAB0EUFrDzI9O58Oz1laAACg6whae5Dtdam8JWhZWdzvEAAAdB1Baw/8KW7VNoXVGAxLWX6pqlImFLK7LAAA0AcQtPYgxxc54mFbXXNk69CEpepKm6sCAAB9AUFrD3J8bkmRoGVxxAMAAOgGgtYe7AhaO92GhyMeAABAFxC09iDb65LD2mnrUOKIBwAA0CUErT1wOiwN8LoiQSs1XXK62DoEAABdQtDqghyfW9vqm2U5HFImRzwAAICuIWh1QY7PHVnRkqSsATIVBC0AALBnBK0uyPG5VVYfVChsIt88rKRHCwAA7BlBqwtyfC6FjSInxLfchscYY3dZAAAgzsU0aIXDYV1//fUqKiqK5bA9lttyxENZXXPkiIdAo9RQZ3NVAAAg3sU0aP3jH//Q4MGDYzlkr/C3nqVVH2w74kEc8QAAAPYgZkFr+/bt+vjjjzV9+vRYDdlrclIiQWsrp8MDAIBuiFnQevrpp3X++efLsqxYDdlrvG6H0pIcLVuHLYeWErQAAMAeuGIxyEcffaSMjAyNHDlSn3/+eafPKy4uVnFxsSSpqKhIfr8/qnW5XK4uj5GXsUGVzZb8o0Zrq6SUpgalRrm+RNWdeUFsMTfxiXmJX8xNfIrlvMQkaK1atUoffvihli1bpqamJjU0NOiBBx7Qz372s3bPKywsVGFhYdvjsrLo3lPQ7/d3eYzsZEslFfXaXl0jpaarftMGNUa5vkTVnXlBbDE38Yl5iV/MTXyKxrzk5+d3eD0mQevcc8/VueeeK0n6/PPP9dJLL+0SsuKd3+fW8s31kWMdMgfIcJYWAADYA87R6qJcn0sNwbDqmsORPq0K/gsFAADsXkxWtHY2YcIETZgwIdbD9ljrNw+31TVrWGa2zLqvbK4IAADEO1a0uijHtyNoKXOAVFMlE2y2uSoAABDPCFpdtCNoBaUBOZGL5WwfAgCAzhG0uijD45TbYWlbXbMsf17kYtkWe4sCAABxjaDVRQ7Lkt/n0rb6Zsk/UJJkyjbbXBUAAIhnBK1uyElxR3q0srIlp4sVLQAAsFsErW7I8bm1rS4oy+GM9GltI2gBAIDOEbS6IcfnUkVDUM0hI/nzZFjRAgAAu0HQ6oYcn1tG0vb6Zln+gWwdAgCA3SJodUPrEQ9b61oa4murZRrrba4KAADEK4JWN+S2BK2y+qCsnMg3D1nVAgAAnSFodcOAlMgdi9pWtCQa4gEAQKcIWt2Q5HQoy+OMHPGQEzm0lIZ4AADQmS7fVHrFihXKzc1Vbm6uKioq9Oyzz8rhcOjcc89VZmZmNGuMK36fW2V1zVJKquRNkbZxaCkAAOhYl1e0nnjiCTkckaf/8Y9/VCgUkmVZevTRR6NWXDzK8bm1tS4oy7Ik/0BWtAAAQKe6vKJVXl4uv9+vUCikTz/9VA899JBcLpcuu+yyaNYXd3J9bn24qVbGmEif1uZNdpcEAADiVJdXtLxeryorK7Vy5UoVFBTI4/FIkoLBYNSKi0f+FJeaQkZVgVDkLK3tWyKhCwAA4Du6vKJ1wgkn6IYbblAwGNTs2bMlSV9++aUGDx4crdriUusRD9vqmpWekyc1NUnVlVJGls2VAQCAeNPloPVf//Vfmjp1qhwOh/LyIt+4y87O1uWXXx614uJRzk5Ba7R/oIwUaYgnaAEAgO/o1vEO+fn5bSFrxYoVqqys1NChQ6NSWLzaEbSCkp8jHgAAQOe6HLTmzJmjL7/8UpL0/PPP6/7779f999+vhQsXRq24eJSa5JDHZUXO0vLnRi6WccQDAADYVZeD1oYNGzR27FhJ0htvvKE5c+botttu0+uvvx614uKRZVnK8bm1rb5ZljtJyszmNjwAAKBDXe7Rav1m3ebNkdWbgoICSVJdXV0UyopvOSnuyNahFDlLi9vwAACADnQ5aI0bN05PPvmkKioqdPDBB0uKhK60tLSoFRevcnxurSlvlCRZ/oEyqz+3uSIAABCPurx1eMUVVyglJUXDhg3TWWedJUkqKSnR97///agVF69yfC5VB0JqDIYjDfEV22WCzXaXBQAA4kyXV7TS0tJ07rnntrs2ZcqUXi+oL8hLTZIkba5p0lD/QMmEpfJtUm6+zZUBAIB40uWgFQwGtXDhQi1ZskQVFRXKysrStGnTdNppp8nl6vLb9AuD0yNBq6SmScNyWs7SKttC0AIAAO10OSE988wz+vrrr3XJJZcoJydH27Zt04IFC1RfX992UnyiyEuLnKVVUt0s5Q+UJJltW2TZWRQAAIg7XQ5a//nPf3T33Xe3Nb/n5+drxIgRuu666xIuaKW4ncryulRS0yRl5kouF0c8AACAXXS5GZ4bJ7eXn+ZWSU2TLIdTys6N3IYHAABgJ11e0TrssMN055136owzzpDf71dZWZkWLFigww47LJr1xa38tCQt3VQbeeAfyG14AADALroctM4//3wtWLBATzzxhCoqKpSdna3DDz9cwWAwmvXFrfy0JFU1hlTXFJI3Z6DM+jV2lwQAAOJMl4OWy+XS2WefrbPPPrvtWlNTky644AKdf/75USkunuXv9M3DUf6BUl2NTEO9LG+KzZUBAIB40eUerY5YVuJ+zy4/rSVoVTfJysmLXGT7EAAA7KRHQSuR5aW5ZUkqrWmW/JEjHmiIBwAAO9vj1uGKFSs6/Vmi9mdJUpLToRyfS5tqmqRRkRUtU8ZZWgAAYIc9Bq2HH354tz/3+/29Vkxfk5+WpNKaJlm+VMnrk8pY0QIAADvsMWg9+OCDsaijTxqUlqQl66ojZ4z5c2XKttpdEgAAiCP0aPXA4PQk1TWHVR0ISTl59GgBAIB2CFo9MGjnbx76B0rbt3KCPgAAaEPQ6oHBO52lJX+e1NwkVVXYXBUAAIgXBK0eyPW55bSkkprmHWdpbS2xtygAABA3CFo94HRYGpiaFFnRGjREkmRKN9pcFQAAiBcErR7KT3OrpLpJyvZLyR6pdIPdJQEAgDhB0Oqh/PTIWVpGkvIKZAhaAACgBUGrh/LTkhQIGZU3BGUNKpDYOgQAAC0IWj2Un77jiAcNGiJVlMk01ttcFQAAiAcErR7KT9txxIPV0hCv0k02VgQAAOIFQauHBqS4lOS0VFrTLA0qkCSZ0m9trgoAAMQDglYPOSxLg1KTtKm6ScoZJDld9GkBAABJBK1ekZ/uVmlNkyynU8odxDcPAQCAJMkVi0Gampo0Z84cBYNBhUIhHXrooTrrrLNiMXRMDEpL0gebahUKm0hD/MZ1dpcEAADiQEyCltvt1pw5c+TxeBQMBnXzzTdr8uTJGjt2bCyGj7rB6UkKhqVtdc3KzR8is+w/Ms3Nstxuu0sDAAA2isnWoWVZ8ng8kqRQKKRQKCTLsmIxdEwM2umbh8orkExY2sI3DwEASHQxWdGSpHA4rP/5n//R5s2bNWPGDI0ZMyZWQ0fd4J2C1gGDhsgocs9Dq2C4rXUBAAB7xSxoORwO3X333aqrq9M999yjb7/9VkOHDm33nOLiYhUXF0uSioqK5Pf7o1qTy+XqlTEGGKOUpLUqb3bKf9D+2mpZSqnertQo199f9da8oPcxN/GJeYlfzE18iuW8xCxotfL5fJowYYI++eSTXYJWYWGhCgsL2x6XlZVFtRa/399rYwxKdeubrdXaXlMjDchV/der1Rjl+vur3pwX9C7mJj4xL/GLuYlP0ZiX/Pz8Dq/HpEerurpadXV1kiLfQFy+fLkGDx4ci6FjJj/NHenRkqRBQzjiAQAAxGZFq6KiQg8++KDC4bCMMTrssMN04IEHxmLomMlPT9K739aoORSWc9AQmS8+lQmHZDmcdpcGAABsEpOgNWzYMN11112xGMo2+WlJChuptLZZBYMKpGCzVLZFyu14KREAAPR/nAzfS4ZmJEuSvq0M7HRzaW7FAwBAIiNo9ZIhGUlyWNK6isBON5emTwsAgERG0OolbqdDBelJWlcZkJWSKmVksaIFAECCI2j1ouGZHq2vbIw84JuHAAAkPIJWLxqWlaytdUHVNoVkDSqQSjfIGGN3WQAAwCYErV40IjPSEL++MiANGiI1NkiV5TZXBQAA7ELQ6kXDsyJBa11FQFZepCFebB8CAJCwCFq9KNvrUlqSY8eKliI3lwYAAImJoNWLLMvSsCyP1lU2Rr51mOKTNrOiBQBAoiJo9bLhmclaXxmQkSLfPCwhaAEAkKgIWr1sRFayGoNGW2qbI31a9GgBAJCwCFq9bFjmjoZ4DRoi1VTJ1NXYXBUAALADQauXDc1IjtyKp7IxcpaWxKoWAAAJiqDVy5JdDg1Ki9yKR4OHS5LMhnW21gQAAOxB0IqC4ZnJka3DbL+Umi6tX2N3SQAAwAYErSgYnpWszbXNagiGpWGjZAhaAAAkJIJWFAxvaYj/trJJ1rDRUsm3Mk0Bm6sCAACxRtCKguGZHkktDfHDRknhsLRxnb1FAQCAmCNoRUGOz6UUtyPSpzVstCTJrP/a5qoAAECsEbSiwLKsSEN8ZUDKzpFS02iIBwAgARG0omR4Vss3DyVp2GhWtAAASEAErSgZnulRQzCsrXXNkYb4UhriAQBINAStKBme1XIrnspApCE+FKIhHgCABEPQipKhGZGgtZ6GeAAAEhZBK0q8bofyUt1aS0M8AAAJi6AVRSNaGuIty5KG0hAPAECiIWhF0fBMj0prmhQIhiN9WqXfyjQ32V0WAACIEYJWFA3LSpaRtL4yIGv4aBriAQBIMAStKBrZ8s3DNeWNOzXE06cFAECiIGhFUa7PrSyvS6u2NezUEE+fFgAAiYKgFUWWZWm836svyxp2NMSvY0ULAIBEQdCKsn1yvNpc26yKhiAN8QAAJBiCVpSNz/FKUmRVaxgN8QAAJBKCVpSNzEqW22Hpy20N0rBRkmiIBwAgURC0osztdGj0AE8kaA3IpSEeAIAEQtCKgfF+r9aUNyoYNi0nxLOiBQBAIiBoxcC4HK+CYaOvywORhvgSGuIBAEgEBK0YGO9vbYivpyEeAIAEQtCKgSyvS3mp7vYN8eu+srkqAAAQbQStGBnv9+rLbQ0y2TlS5gDpq5V2lwQAAKKMoBUj43O8qmgMaVt9UNbYiTKrPpMxxu6yAABAFBG0YqT14NIvtjVI4yZK1ZXSlk02VwUAAKKJoBUjQzOS5XE5tKqsQda4/SRJZtUKm6sCAADRRNCKEafD0jh/y8GluYOkzGxp1Wd2lwUAAKKIoBVD43O8WlcZUGPQRPq0Vn9OnxYAAP0YQSuGxvu9Chvpq+0tfVpV5dKWErvLAgAAUULQiqGxfq8sSV9ua5A1tqVPazXbhwAA9FcErRhKTXJqaEayvixrkAbmSxnZEg3xAAD0WwStGBuf49WXZQ0ykqyxE2RWr6BPCwCAfoqgFWPjc7yqawprY3WTNG4/qbJc2lpqd1kAACAKXLEYpKysTA8++KAqKytlWZYKCwv1/e9/PxZDx51xLTeY/mJrg4aMmygjyaz6TNbAfHsLAwAAvS4mQcvpdOqCCy7QyJEj1dDQoF/+8peaNGmSCgoKYjF8XMlPc2uA16VPNtfp+NGDpfTMSJ/WtBl2lwYAAHpZTLYOs7KyNHLkSEmS1+vV4MGDVV5eHouh445lWTog36dPS+sUNpI1bj/6tAAA6Kdi3qO1detWrV27VqNHj4710HFjSr5Pdc1hrS5rkMZOlCq3S9vo0wIAoL+JydZhq8bGRs2dO1ezZ89WSkrKLj8vLi5WcXGxJKmoqEh+vz+q9bhcrqiP0ZFj0zI1950SfVFldNih39P2Zx+Wb9M6pew7Kea1xCO75gV7xtzEJ+YlfjE38SmW8xKzoBUMBjV37lx973vf0yGHHNLhcwoLC1VYWNj2uKysLKo1+f3+qI/RmbF+r95ds02njh4mpWeq9uP/qP6Aw22pJd7YOS/YPeYmPjEv8Yu5iU/RmJf8/I6/1BaTrUNjjB555BENHjxYJ598ciyGjHtTBvm0prxRVYFQ5L6Hq+jTAgCgv4lJ0Fq1apWWLFmiFStW6LrrrtN1112njz/+OBZDx60D8n2SpE9K6yL3Pawok7ZttrkqAADQm2KydTh+/Hj95S9/icVQfcaobI/Sk51aVlKno8btt+M8rdxBdpcGAAB6CSfD28RhWTpgkE/LSusUHjhYysiSVn5id1kAAKAXEbRsNCXfp6pASGsrmmRNOlhmxUcyzc12lwUAAHoJQctGkwdF+rQ+Lq2VNfkQqbFBWrXc5qoAAEBvIWjZKNPj0qhsj5aV1En77C8le2Q+ed/usgAAQC8haNlsyiCfvixrUJ1xShOmyHy6VCYctrssAADQCwhaNpuS71PYSMs310W2DyvLpfVf210WAADoBQQtm431e5XidujjkjpZkw6SHA62DwEA6CcIWjZzOSztn5eij0vrpJRUacwEmU/+Y3dZAACgFxC04sCU/FRtrw9qQ1WTrMlTpZJvZbaW2l0WAADoIYJWHDig5ZiHj0pqZe0fueE224cAAPR9BK04kONza0RWsv79bY2snDypYLjMpwQtAAD6OoJWnJg2LF2rtzeqtKYp8u3Dr76Qqam2uywAANADBK048b3h6ZKkt9dVR4KWCcss/8DmqgAAQE8QtOJEjs+tfXO8emtdtcyQkVKWnz4tAAD6OIJWHJk2PF0bq5u0rrLl24crl8k0BewuCwAA7CWCVhw5YmianJa0pHX7sCkgffGp3WUBAIC9RNCKI+kelw4Y5NPb66sVHjNB8qbIfPye3WUBAIC9RNCKM9OGp6usPqgvK4Kyphwu89G7Mo31dpcFAAD2AkErzkwtSFOy04psH37veCnQKPPBO3aXBQAA9gJBK8543Q5NLUjVu+ur1TxsrJQ/VObt1+wuCwAA7AWCVhyaNjxdNU1hLd9SL+t7x0lrV8tsXGd3WQAAoJsIWnHogEGpSk1y6K111bIOPUZyuWTeed3usgAAQDcRtOKQ22npiKHpen9DjQKeVFkHHCbz3mKZ5ia7SwMAAN1A0IpT04anKxAyWrqxNtIUX1/LUQ8AAPQxBK04tW+uVwNSXHrjmypp3H5STh5N8QAA9DEErTjlsCzNGJ2pT0rrtLGmWdaRx0mrPpPZUmJ3aQAAoIsIWnFsxphMuR2WXvqyQtbh0yWHg6Z4AAD6EIJWHMv0uHTUiHQtXlulGm+GNOlgmX+/IRMM2l0aAADoAoJWnJs5LktNIaPX1lTK8b3jpepKafkHdpcFAAC6gKAV54ZneTQpL0X/WFWh4D4HSJkDFH7rVbvLAgAAXUDQ6gN+MD5b2xuCem9Tvaxjvi+tXCazdrXdZQEAgD0gaPUBU/J9yk9z68Uvy6Vjvi+lpin84v/ZXRYAANgDglYf4LAsnTwuW19tb9TqWoes40+VVnwk8/WXdpcGAAB2g6DVRxw7MkO+JIde/LJc1jEnSanpCr/EqhYAAPGMoNVHeN0OHT8qU+9tqFFZyCVrxqnS58tY1QIAII4RtPqQk8ZlSZJeXlURWdVKy1D4xT/ZXBUAAOgMQasPyfG5dcTQNL36VYXKQ05ZM06TVn4is2al3aUBAIAOELT6mPP2z1EwLD37aZmso09sWdWiVwsAgHhE0OpjBqUl6eRxWXrzmyp9UydZJ5wuffGpzOrP7S4NAAB8B0GrDzpr4gClJTv1xEdbpGknSOmZCr/wrIwxdpcGAAB2QtDqg3xJTp07ya/Ptzbo/a3Nsk46S1q9Qlr2nt2lAQCAnRC0+qjjR2dqaEaSnl62VcEjZ0iDhyn8lydlAgG7SwMAAC0IWn2U02HpwgMHanNts15ZUy3HuZdJ27fKvLrA7tIAAEALglYfdsAgnw7M9+kvK7areuh4WVOPknl1gcy2zXaXBgAARNDq8y6ckqvGYFh/Wl4m68zZktOl8J//1+6yAACACFp9XkFGsk4am6V/flWplU1eWTPPlj5dKvPZh3aXBgBAwiNo9QPn7Z+jgalu3ffvUjVMO0nKG6zwc4/LNDfbXRoAAAmNoNUPeN0OXX34IJXVN+vJT8vlOOdSaWupzOvP210aAAAJjaDVT+yTk6LT9h2g4q+rtDR9tDTlMJlX/iJT8q3dpQEAkLAIWv3IOfv5NSIrWQ++v1nVp10sJXsUfrhIprHe7tIAAEhIBK1+xO209PPD81XXHNbDXwZkXXq9tLVE4acf4PY8AADYICZB66GHHtLFF1+sa6+9NhbDJbRhmcm6YLJf72+s1ZvuIbJO+5H00b9lXn/B7tIAAEg4MQlaRx99tG688cZYDAVJp4zP1sRcrx7/cItW7D9DOuBQmQVPy6xeYXdpAAAklJgErX333VepqamxGAqSHJala47IV47Prd8u3qj3jrtE8ucp/NjdMpXldpcHAEDCoEernxqQ4tYdxw3TmAEe3bN0u179wXVSQ73Cj94l09xkd3kAACQEy8SoS3rr1q268847NXfu3E6fU1xcrOLiYklSUVGRmpqiGwhcLpeCwWBUx7BbY3NINy9apXfXluu8vGad9tyvlLTP/sr85R1ypGfaXV6HEmFe+irmJj4xL/GLuYlP0ZiXpKSkjsfq1VF6qLCwUIWFhW2Py8rKojqe3++P+hjx4NpDc+R1hPTs11Xa+oPf6tJXbtO26y6S42dzZA3Mt7u8XSTKvPRFzE18Yl7iF3MTn6IxL/n5Hf/7lK3DBOB0WPrvQ/J05oQBer3KqydPu0Wmvlbhoutk1qy0uzwAAPqtmASt++67TzfddJNKSkp0+eWX680334zFsNiJZVk6f3KOTt0nW4u2Wlpw9q1SSprCc3+t8Adv210eAAD9Uky2Dq+++upYDIMu+NEBOapoDOpPX1cr69w5mv7y/TKP3a1wdZUc00+2uzwAAPoVtg4TjMOydOWhg3Rgvk8Pf1qlD87+pTT5UJnnHlP4NW5CDQBAbyJoJSCXw9L13xusUdkezf3PFn152pWyDjxC5q9PKrzob3aXBwBAv0HQSlAel0M3H12gHJ9bt71TqpU/uELW1KNkFv5R4Zees7s8AAD6BYJWAkv3uPSbY4YoI9mpXy/eqL8e8iOFDztW5sU/Kfz3Z2TCYbtLBACgT4urc7QQe7mpbs09cbgeXrpFf/psu1YMPU1Xu1KU+Y+/yCz6q+RNkbw+KcUn+dLkOOb7sqYcbnfZ/Y4p3Sh5vbIyB9hdiowx+uMn23TQ4FRNyE2xuxwA6NNY0YJS3E5dc/ggXXlonr4sa9DPU47Rp+f8j6yTzpZ16DGyxkyQsvxSeZnCj9yl8L/fsLvkfsWEwwrfe5PCT8yzuxRJ0qaaJi1cWa6Fn2+3uxQA6PNY0YKkyDlbhaMyNXaAV3e/s0m/3TxABw8eqmMmZ+jgglQlOR0ygYDCD94q8/QDCgeb5Zh2gt1l9w/r10iV5VJVhUz5NlnZObaW89GmOknS8i31agyG5XHx32MAsLf4f1C0MzQzWfecMFxnThigb8oDuuudEs1esEYPvl+qlVUhWf99kzTxQJn5Dyn8xst2l9snvbKqQmsrGtsem+UfSJYlGSPz/ls2Vhbx4aZaJTktNYWMPi2ts7scAOjTCFrYRbLLofMn5+jx/xql3x47RFMLUrVkXbVufP1b/epfm1Vy3rU7zt7659/tLrdPWVfRqMc+3KKnl21ru2Y+XSqN2kcavY/Me4sVo/u8d6i+OaTPt9brhDGZ8rkdWrqp1rZaAKA/YOsQnXI6LE0e5NPkQT5d1hzWW+uqNP+Tbbr6tY0668iL9V8ut1x/e0rh6gpZp5wnKznZ7pLj3qtfVUqSPi2t05baJuU2VUsb1so6fZaU4pOZ/5D07dfSsNG21LestE4hIx06JE0VDUF9sKlWYWPksCxb6gGAvo4VLXSJ1+3QCWOy9ODJI3XokFT9aUW5fjH4dH111Fkyrz2v8G/+W2bFR3aXGdfqm0NavLZak/Ii3+R745uqyLahJGvSwbIOPFJyuWTeW2xbjR9uqlNqkkPj/V4dPDhVVY0hfbW9cc8vBBBVxhi9sqpCv3xtvcobgnaXg24gaKFbMr0uXXfkYN141GDVNYX1S+sgPXh6kdb58hS+/7cKP3qXTGW53WXGpbfWVqsxGNb5++fogEE+FX9dpeDyD6WcPGnQEFm+VGnSVJmlS2SCsf8/0rAx+qikVgcM8snpsHRgfqoclrR0I9uHgJ2aQ2H9/v3NeuzDLfpiW4MeeK/U1haD3mCM0dfljQoE+/95Cz/y6gAAIABJREFUjQQt7JVDCtL0+5kjdPK4LL1d6dQ1I87TzdNv1n++rVbzzVco/M+FMnX8C7qVMUb/XFOpEVnJGjvAo+NGZ2h7fVCfbq6PrGa1bM05DjtaqqmSVi6LeY1flzeqqjGkgwanSpJSk52akJuipRtrYl4LgIjyhqB+Vfz/27vv+LiqM+HjvzNNGnXNjHqxbLnK3cgFY7DBpoQOAUIIbAxeSHCWGpwY9t1AKDEEWBsIxEAcYDdhExIChGKKcQP3irvcZFldmhm10fS55/3jCtmO5IqkEeh8P/ZHZa7uPTPP3HufObWcJQeauGGEnTuKM9hS3cqHexuiXbQzpknJHzfXcf/iQ9z6zn5e2VjL4cZAlx8nGNHYVOnhnV3RnapG9dFSzlic2ci/F2fwg5EOPtvfyId7TTxVdDPpEQ+XrFvO9I/uJOmsiYhplyLyB0S7uFFV4vRT2hDgzgkZCCEYn5NIslFjSfo4ikcNOrLhiLMgIRG5djli1PgeLePGSg8GAeOy4tt/NyE3gUWb6qhpCZKZaOnR8ihKX7fX6WPeykpagxF+MSWbc/olIdtqnt/YUs+ojHjyU75Z39gGXxhvSCMt3oTF2P11L2FN8sKaapYfamZGYTLBsOSTfY18WNLAsDQrFw1MITvRgkTS9g+AfikxxFuMJ91/oz/MxkoPGyo9bK1uxR+WxJkNXDo4lZgoTVWjEi3lG0uMMXLtcDtXDbOxrqKF9/c08D/Gy/m/Ad9jcv12Ln5uPkPSrBimXYo46xyE2RztIve4xfsasJoMTC1IBsBsFEwLlfOBvYjG/AGktm0nTGbE+HORX3yG9LYi4uKPv9MutqGylcF2K0mxRy4L43P0RGtDpYcrhtp6rCzKd4uUEn9YYjV3340uokk+3d9InNnA1P7J3XacnhAIa3xY0sCb25ykWk08dXE/+qfGAvqch3dPyuLuD0v579VVPH1xP8xnkCBJKfnsQBOvbKglpOnpTHKskfR4M444M7EmfYqXYERr+ypJjDEyNiues7LjyUjo+MErokkqm4M0+sMMTbN2SNz8YY2nVlayubqVm0c7uG64HSEEt/vDfH6wiU/3N/LcmupOyxtnNnD5kFSuHGojMebYhCuiSTZWefiwpIFtNV4kYI8zcX7/ZMbnJDAyM65HksjjUYmW0mWMBsHk/CQm5ydxqMHPJ/sbWWYey4r0MfTz1zPls41kfraa9GFDSZ98DqlZ6e1NZl+TLc2EW5uQcUkdHustSpw+/rbDyfUjHAxxWE+6fXMgwqqyFmYUJrffaKSUTN/zKe8V3c6yw16uLTqyHzHpfOSyj5CbViHOvajbnsfR3L4wB9x+bh7tOOb3WYkW8pItrK9QiZZyZiqbg7yyoYYddV4ePC+3vWm6Kx1w+3lxXTUH3HrzU0VzkJtGOU54DYloEqPhm19jpJTsd/v5sqyF8wqSKLTFnvG+QhE9WfzbDicN/gjjc+K5e1LWMR9+QO8r+x+TMnliRSV/+srJrePST+s4vpDGwvU1LD/UzJjMOKb2T8bZGqLeG6KuNczhpgChiMRiFFiMArPRQIxRUNYYaO+zmZtk4azseHKSYjjU6OeAW6+1D0b0pC3WZGBCTgKT+yUyLiueQETy2LJy9rv9/GxiJhcNTGkvT1KsiWuK7Fw9zMZel5/WYATQk0qBXgu25EATb+1w8f6eBi4bkspVQ1MxGASfH2jiw70N1HpC2ONM/GCknYm5ifRPjek19xCVaCndoiA1lp+Mz+TfxqTzRVkzi/fG8OfYthnPA8CyBszSSbYlwnhZz9n1Oygo24JodOMCyMhBTL0EMfkCRHxiFJ/JsWpagjy+vILmQIRNVa1cW2TnxpH2E36iXHqwkZAmuWTQkQsLhw+QW7ePoWMDLDnQxDXDbEcuCv0HQ0YOcu1y6KFEa3OVfvHs7CY4ISeBd3a78QQiODo8qpyOQFhj4YYa8pJiuKbI1mtuBN0hENb4+04X/9jlxmIUZCRYeOqLSh6bns/QtJN/QDkVvpDGm9vq+aCkgeQYIw+ck83Wmlbe2uGi0R/mp+MzOyRTh5sCvLqhlh11XjITLPRLiaEgJYZ+KTEMsMV0WlPTGU8gwopDzXx2oJHSBj3BW17axDOXFJAWf3q19hFNsry0ib9sd1HXGqIozcqcc9NOuNbohNxELh6Ywnu73ZyVHc+ozPj2fbl9YRp8YdLizaTEGo95nx1uCvDUykoq25LR64bbTznhlFJS2RJkc1Urm6pa+Wivfm2zmgwU2mK4ZFAKhbZY4s1G1lW0sLbCw8qyZmJNBuLNBpoDEX55bg6T8jq/pgshjvvhtTgngbLGAG/tcPL2ThcflOiDrvxhSVGalR+PSWNSXmKXJM9dTSVaSreymg1cNDCFiwam0BqMUN8aoq7GSd32HdSXV3EgJo1/pBTy99QcspKncHa8jwsyTGR88Q7GtxYh3/lfxPhz9ZqdvAFRnavLE4jw6PIKpJQ8e0kBi/c18PedLjZWerh3clZ71f7RNCn5eF8jw9KsFBz1uPxKnw3+wmHpvLC1id31PoraLqpCCMSkacj3/ox01SHs+qdVty/MS+tqqPUEeWhqLlld2GdqY6UHe5yJgk76e0zITeTtXW42V7dSkNNlh+xzvKEITyyvYEedDwBfWDtprcu31cZKD69srKXWE2JaQRIzx6UjBMz9tIzHlpcz78J+36hvkcsbYluNlz9/VU+9N8wlg1K4ZUwaCRYjU/olkhJr4u87XTT6IzxwTjYxJgPeUIS/bnfx/h43VrOBy4ak4mwNUdboZ215S3tfoLxkC5NyE5mUl0ih7UitSFiTHG4MUOL0sbPOy9pyDyFNUmiL5afjMxhgi+WRpeX8ZkUF8y7qd8pLV+2o9fLKxlrKGgMU2mK5c0IGY7PiT+l9cdtZ6Wyv9fLMqipykyzUt4ZwesNoRw1ITIox0q8tkUyMMfKPnS5izQZ+PT2P0Zmn1zVBCEFuUgy5STFcOdSGP6zR5NcTun+da298bgI/1SQ7ar2sOtxMWWOQn5+TzfCMM1+ovl9KDHOm5PCDkQHe3eVGCLh0cOo3qkXsCUL24jGiVVVV3bp/h8OB0+ns1mMoxydDIXDW0JSQxvpaP6sOt7CtphVNgtVkYGiiZKjrAEN3r2CQaz+xWgjiEiDVDqkOhM2BKBoLo8Z3e7+vUETyyLJy9tT7eHR6XvsnzfUVLby4rgZPMMIPRjq4qDCFFOuRzy9bq1t5eGk5903OYtpR/UYij98PJhOhB55k5tv7OTs/gXvOzj7y2jhr0R68HXHxNYgrbmJNbYCX1tcSCGtYjAKjQfCraXkMtJ/6BUZKSWlDgH4pMcd86gtFNG7++36mFiQxe2Jmh7+LaJJb/7aHUS2H+H/9Q/inX3lar11nQhHJX7c7WV7axKyzMjg7//RrLcsaA6THm7u1309X8QQjPLqsnH0uP/eencX2Wi+ftY0i64pkq7dcy3whjZc31LCstJncJAs/GZ/RXtMCUOsJ8stPyjAIwVMX9zum5qclEOHDvQ0s2d9IrNlAZoKZjAQLmQlmHPFm6jwh9jh9lDh9uLz69Cf5yRZmT8xkWFrHm/cHJW7+sLGOoWlWLixM5k9fOXH7wswoTObfxqSRfFRzXCCscbgpwJ56H+sqPOys86JJSIszMTIzjuqWEAfc/vZmseQYI5PzE7loYAoDjrrJb6z08PjyCs7OT2TOlGwMQhw3Nk5viNc31/FFWQvp8SZmjk1ncn7iab8XDrr9vLS+BrNB6P2r4s2kx5tJsRqp84QoawxQ1hjgcFMAf1gyIt3Kz6fkYLP27XqW7jhnsrOzO/29SrR6wcVJOaI5EGFfi2D9wTp21/s43BhAAgYkDkOIdK2VjEAjGa1O0hvKyWiqIl0ESB07DsPZF0D/wV1eQyCl5Lk11Swrbea+yVlMTfQjv/hUHxU4YAgtgQgLN9Sy6rA+DUJ2opmi9DiGpVlZVdbCfrefP15T2N68KBtdaHNuRVxzC4ZLr+eldTUsK23i9WsHHjOqJjL/YVpLdrNo8FUszziLgVoT96Q3Yhg4jF9vD9Ic0HjwvBzGZJ38U2lNS5DfrTjI9iboH3Qxq2EtRaE6QPBVbA6/dlzIQ0UmJowpPOb1kzWVaG+/we98eaxNG8Hrqx/FMuc3iIHDOn2dDjYE2FDpIRDWuGhgSqe1boebAixYXcUBdwB7nAmXN8zVw2zcMiYN0ylU+x90+/nTV/VsqmolI8HMz8/JPqW+ct1JSkmNJ4TNauowsqnZH+bhpeUcbgrwwJQczs5LRJOSl9bV8NmBJq4fbudHo79ZstUV1zJfSGNbbStbqlrZUt2KJiVp8WbS2m7cafFmhjis9DtOTdShBj9Pf1lFVUuQG0bYuW64A7Ox43M61ODnoc8Ok2o1Me/CfMIS3tvt5uN9jfjDGuOy4rGYBDUtIWo8IfxHzbOUHm9mqMPKYEcsQxxWCm2xJ2wq+rKsmfmrqwlrkkJbDD8Zn3lq/Sr9YdZXelhb7mGP00dOooXBjlgG2a0MccSSHm8+brze3e3itc31/HCkgxtHOTrEJhTReG9PA3/b4SSiwbXDbXy/yN7tI+I0KWn0R0iJNaqVHlCJVjuVaPVNR8fFE4i0f4qt8YSo9YSo9QRp9EeO+RtLJEi6v4F06SXBCDHhAJaQH0vIR0zQR2KMibSCPBwjikjLzyXRYiCkSQ66A+x1+djr9LHP5ccb0hhkj2VompWhDiuD7Fb+ucfNm9uc3DTKwQ1pfrT//hU0tL1vsvMR514IE6dxIGhhe62XXXU+dtd78QT1G8Q1w2zMPKqzqvbFp8j/+R2Gh59H5Bawz+XjgY/LmFqQRH5yDEK0rTEdCrG4xI0rJLjOu5Prdr+PqaUBzBYa73yERw/HU9Ec4O5JWccdZRXRJB/tbeB/N9dgCAX5nnMLK9NG4TTGc47vED9u2cw/YwfxScJQ3vjyEWIdDn1k6Mhi5IYvkCs/BpOF9dNv5UlvAY8eeosR/hrEf80nZDDjC2nsd/tZX+FhY6UHly+MAAwCNKn3q7hiaCqjMuKQwAclDfzPlnqsZgOzJ2ZSnB3Pok11LN7XSFGalQemZGOP67x2sqo5yJ+31fNlWQsJFn2lgpWHmnB6w9w0ysG1Rafe16QraFKyz+Vn9eEWVh9uoa41hMkgGOKIZWRGHCMz4kmPN/PY8nJqPCEePC+HcdkJx/z979fX8On+b55sORwOauvqCUQ0gmFJIKIR0iRJMSYSLYYO+41oklpPiPLmAOWNQb6qbWVXnY+wJok1CUZmxBNnNuhN/a0h3L4jzVFFaVYuG5LKpLxETAbRPnrt1Y21xJsN3H9O9jG1WJ3ZWevl4aXlpFpNbfuWTOmXxPeLbMc2sUtJcyBCXWsIR5yZ1DOohSlx+qhqDnJeQVKPvD+klDy/toalB5v4xZRsrjprAJU1dWyr8bK2ooX1FR6aAxEm5iYw66z0U+4TpnQtlWi1UYlW33QqcfGHNeo8+k2g1hOiptFLbWUtdc1+vMJEUJgIGMwEMRIWHT8pxhAhIoyE29799jgTg+1W4i0GSpw+ypuCwJGE4fz+Sdyd50fOf1j//ewHkVXlyC8/g9K9YDIhxp6NuPpHiPRsNCmpaApysMHPhNwE4sxH1VT97nEoL8Xw5B8QQr9R/eeSw+xs67tztOxEC/dOzmKIw6rPBO2sRXvhMWhw4rvnMeYdimVHrZcbRtgZlmYlJdZEqtVEUoyRqpYgL6yppsTlZ5xrNz+N7Cb9339G0JrI27tcvLNL70xqMQoGp5j5r5i9yE2rYM820DQwGBDnXYy44kYCccnc/Ld9mIWEgA+/KRaNIzetWJOBsVlxjM9J4KycBDQJH+9r4OO9jTQFIvRLjiHeYmBXvY/xOQn8x8TMY5pYV5Q28eK6GmLNBu6fnE1GghmXN4zLG8LlC1PWGGDloWbMBsGVQ21cXWQjwWLEE4zw+/U1fFnWwoiMOO4dbMCRmoRITCIQ1qhoDnK4MUBLMIJAT2AFAiH04eI5SRZykizHxEdKPQn5OsGvaA5iNghiTYa2/4KwBhurPLi8YUwGGJ0ZT3FOArWeENtrvRx0+9v7/MSaBP85NbfT5OPoZEsfwWXBZjXp/+NMGBBUtQSpaglS3RKkqiVEQ1tioknav0aknjx1xmTgmPeFszVMZUuQ8FHb5ydbGJedwLjseIrSrB0Gd4Q1ibM1xJryFhbva6TWEyLVauKSgSlUtQRZ0TZ67b7J2cfE9UTWlevN7pPyErmmyNalfQ6jLRTR+M8l5ZQ2+JlUYGN9mbt9Pqfi7ASmFyafUk200n1UotVGJVp9U1fHJaJJmgIR6qvrce7aTX1ZOc6WAGYtzCBPBYNiI9iz0yGvP6L/YCgcRmsY9rp87HH6kBKuT2zA+NwjYLFguP9xRFZu+/5lRSnyyyXIVUtAiyCu+hFi+pUI47FzvciWZuQ//4xc8Qnigssw3Hj7kcekRG8h0W+cEv0mGmsydKjmlw0utN/OBZ+X8P1P8FyZub3Z8msGAUiI0/zMKnmHqaP6Yfj+j48pU50nxOtb6lh1uIX/mJjJhW3DrWVLM3L3VkTegGOe58f7Gij3ANvWEVt1kLipF2J1OMhKtDAivePNGfSZmb841Mz7JQ3UeULcOi6dGYXJndbcHG4M8OQX+miofxVnNnB+/ySuH+HoUKshpeTzg028ur4aU8DHUG8lFRmDqfVLTvXilmo1kZtkIdYk2OvSZ8gHPYHMT7YQkXo/Hn/bf03CyIw4JucnUpyTQMK/TKToCUTYWedln8vPxLwEBtmP31ylScn/bq1nbXkLLm+YQKRjqU0GyEywkJVoxh5nxmgQGIS+tIdBCOLj49CCfmJMghijgRiTAYPQ+z25fWEa/WHcvgjN/jD2OJPeoTnZ0tax2UJCzMkngvxaRJNsqW7lo70NbKpqxSDgh22j11ST1BGNvjC/+LSMsBSMz45jUl4iI9LjOm1OVXqeSrTaqESrb+qJuMhGFxzYgzxciqwohfLSI82BCUmI0eMRY8+GojFwaD/a87+G+EQMP38ckdaxwzi0JUB//j18tR4KBmH48V2I3AJkOIxc/iHy/b+A34eY+j3ENbcgrGc++kbW16A9NReQiDnzqItz0OAL0+AP09DYSkNpKdr+PVxWvoLUm2ZhmHDecffl9IawW02n1GzlcDioP3QQ7Vc/g1QHhoee6ZBQdlretsvMyY7hDUVYUdpMjMmAzWrCHqf/P7rGqdP9b99ExR9f5vdFP6BZWMjz1pE/ejj5/bPJS44hNdakJ11S0pbP0hKMUNkcpKI5SGVzkMrmAK1BjYH2WIY6rAxNs5KfHNOjzZFSSnxhDbc33N5cl5WoTyB5onJE61pW0xJEk5Cd9N2pjepKEU2SlubA7YruEjBKRyrRaqMSrb4pWnGRnmYo2YHcvAa5fQP4vBATC1KD1DQM9z+GsJ14JikpJXLjKuT/vQxeD2Lq95C7tkBNJRSNxXDDLEROfteUt/Iw2jMPQowVw/2PweEDaGuXw45NEIlAXn8Mt96LyOvfJceDI7GRm1ahLXwK8f0fY7jk+122/zMhv9qAtnAeZPfDcP+j4G1FW/AwNLox3DkXMeKsqJavJ6hrWe+lYtM7qUSrjUq0+qbeEBcZDsGe7cgta8Dbivjh7Yik1JP/4dd/72lG/nURcu0ySM/GcMMsGFXc9SMiy/ajPfOf4G/r35ViQ0yYijh7GiK36xKsr7UnWlKi/X4e7NiM4VfPITKjM8GW3LoObeFTkFuA4b5HEfF6Z3PZ3ID23K+hsgxx670YJk6NSvl6Sm84Z5TOqdj0TirRaqMSrb7puxQXWVMBjgyEqfvm+ZIH9uiLUI+dBENHIgyn3t/mdB0dG9no1psQI2HIzEFk5EBGjv59WibYHJCUijCc+rB16ffBwRIYMBgRe/ymVSklbFqF9odnIb8Qw72PIOKOndVe+rxoLz4BJdsRV96EuOhqREzvntjwTH2XzpnvGhWb3kklWm1UotU3qbj0Xv8aG3mwBLl+JbK2Um8eddXB0ZcUoxFS2iaYTcuEoaMQRaMRKfZj9isPH0Cu+AS5bgUEfBBj1Wvlpn4PkVtwZDuvB7luBXLlp1BRCgOGYLjnkeMuvi1DQeRrzyE3fAGJyYhLrkVMvTSqKwx0B3XO9F4qNr2TSrTaqESrb1Jx6b1OFhsZCkJdDThrkG6nPsCgwal/X3UYPM36htn5iKIx4MjQ13Q8tA/MFkTxFMSYCcit65Ebv4RQEAYOQ0w6Hw7s1qefCAYhv1CfemLS+aeUNMn9u9De/wvs2gpJKYhLvo8YPwWaGsFdr5fPXQ8tjRDRQIsgtciRaS7SsiArF5GZC5m5iPgEpN8LddXI2mqoq4JGlz4H2dBRJy5LOATCcEqDCE6VOmeiT+7dgayt0t8zUmv7CrapF9Jo+m4l9t8FKtFqoxKtvknFpff6JrGRmgYVpchdW5G7tsK+XRAO6UnXeZcgJk1r72MFbf3cVn+OXPEx1FVDrBUxcSri3IsR/QrPrAz7dqG9/3+w+6uOD5rMkJQCJhMYjGAw6F/D+lJRhMNHtrXG6YMljmaJgWAAxk3GcN3MDqNTZaMbueQ95PKPwWJBTJ6OOPciREbnF+fToc6Z6JK7v0L77//q9DFDig3mzEOkZ/VwqZQTUYlWG5Vo9U0qLr1XV8ZGBgPgdkJG9gkHCegJ2iF9uy7qYyX370KWHUSk2sGeBrY0fVqP45RDRiLgqoXqSmRNud5EmupApGdDRhakZYEQyE/fRS7+O2ga4qJrEJdeB82NyE/fQX65BCIRRPE5es3ftg16rceQkYgpFyLOmowwn9k0CR2adJsaYP9uvc9e/OmvI/ltJLUIlOzQ49iFI21Pely/D+2Ru8BownDfr9sSdQMIg15buuARpDUOwy+eRKTYeqxcyompRKuNSrT6JhWX3kvF5uSk24n8xxt6f7PEZGht0ZsKJ1+g9xFL1y/GstGFXPW5vrqAs1avsRs1AVF8Dgwfi7B0bG6SrS36/lLTjllI3eFwUF9dpTe5rlkKOzfrSZwlRj/u9Cv0Zs+eeg1CIb0p1lmLdNbqzy8UREy9BJGV17XHqqvWaz5XL9WbqoXQBz5c9aMzTlxPh/bmy8jlH2GYMw8xqKjD48nuWty/ugvSsjDMeaLDoA0lOlSi1UYlWn2TikvvpWJz6uT+3Wgfv41Iy9Jv/Kn2zrfTNNizTV9bcstaPZGKsSJGFUNOv7Z+YJVQWwmethUAhIBUOzgyEWkZxFjj8K9eCt5WSLHrAwmGjtIHKqxboTd7jizGMOPK0x6ZKhtcyC1rkFvXgdGImDhNX27qX/rGyVAIuXk18otPYe+OjoMihEGv0Zt4HuKKG9sTztMlw2GoKkMe3KsPcti7Q9/38LGIydP113Llx5CVh2HWfYh+A8/oOKdUlr070Z5+EHHB5Rh+eEen2zgcDupXfKYvnTVgMIZ7f91pEq30LJVotVGJVt+k4tJ7qdh0LxkOw97tyE2rkZvX6IMHUmyQkaP35crIhvhEvemy/uvaohqE3w+jxyMmX6CP7DwqkZLNDcjlHyOXfwQtTRBrhQFDEAOLEAOHQf/B+sS8wSAEvPqcbD4vsmS7XoYDe/QdZeXpfdBcdXrtW/EUPbGJT0B+8Rly7VI9EbSnIyacp0/z4cgAewak2qDVg/zkH8hlH0I4jDj7AsRlNxx3pYX28rc0I3dugoMlyEP79VUcwiH9wbRMxDkz9H0dNZmw3LEZ7Y0XoLlBP8alNyBMp78g9QnLFQigPXo3aBqGR144brP21+eMtuEL5KvPwKjxGO58sEsHQ5xaef3IdSsQw8ch7Gk9euzeSCVabVSi1TepuPReKjY9R0YiEA6dUr+0U4mLDAX1Wqm9O5D7d0NlmV7r9PWi61Lr+Ef5AxDjJiPGnY3IytNr3/bt0pvqNq2CgF/fzmhCjJmIOO8iGDr6hHOnyaYG5OK/64Mc2lYwEIPakr6BwxApdmR9DXLrOr28+3bpZYu1Qr+Beg1VQdvXtMzj96tr9SD/8qo+aXBWHuLCq/QBF13UnKj97Y/IT9/VV4wYNvq42x0dG23Zh8g3X4ZhozFcfC0MO/Fr1RVkOIRc+Qnyw7eguVGfQPmhp781ffeklMilH+qjjls9eo2vV/8qho9D3HbvGc1TqBKtNirR6ptUXHovFZve6UziIr0evZboYIk+pUWstf2/iLVCbsEJa5uk36fXePm8iPFTEEkpp3d8txP5xafIfTuhtESvUQO9X1tLk/59Tj89gRszUZ/S4wySErl1Hdp7b+rzriUmI6Z9T/9/Gis9dNjngT1oT81FnHsRhltmn3Dbf42NtvQD5Ad/1Z9jerZelsnTjxlxe9zjSqn3ffM0g6flSJ89vx9S7Xq80rMgIQmkhly3EvnPN/U+coOHIyZMRf7lFSgcpk/ye4oJipQSuW45VB5GDCyCQUXHnbuuq2mL30b+4w2wp+ujguMT9ddKCH1qmLMmY7h9zmnXEKpEq41KtPomFZfeS8Wmd/q2x0WGw1B+UK9pO3xQr+UaM7HLpkSQUsKebWhL/qmP9jSZYEQxIjHpmAQTk1mftsPbCl4P0tsKfq8+zYfJrA9AMJmQ+3ZBJILh17876eLwncWmvT/bsg/1plmLBXHWFMTEqXrT778kDXqz33Lkso/0EbgnY43Tm4Mb3ZA/AMM1t8DwcQgh0NYuQy6arze5/viuky4LJt31aP/7IuzYrNd+Sk3/2q8QMWQEYtAIyOuvJ3onGT18uomytn4l8tVnEOPPRfz7zzv8vfbZe8i3FiHOPh8x854Oj0stojdr79iMYfaDx5SvJxOtrm20VhRFUZTTJEwm6D8Y0X9w9+xfCBg2GuOw0ciaCuTn7yN3faVPOuv36X3PjmaxQFwCWOP1pKWtGVeGw3r/MIMBw601RBOsAAAOW0lEQVT3nDTJOm55zGY9qZo4FXn4IHL5R8iNX+ojRhOS9Kk+xp8HyanIFR8jVy0BXyvk9kf84N8RjnSIT4KERIhP0OdwczuhvgZZXw11NcimBkTxOfokukclIIZJ56PVVum1apk5iOMsCi+lRH75GfJvf9QHMfzwDsQ5F0JpCbJkB7JkG3LJ+8hP3tH/ICFRL19uf3Ck6yNAnXV6P0JXLfh8iAnnIi7+PiIn/6Svkdy7E/naAr327NaOSRSA4cKr0AJ+5Ht/1l+DH93ZnkzJsv1of/q9Phny4BF68nwKtYbdQdVofYs/BX5Xqbj0Xio2vZOKyzcjtQgEAvpKBNb4Y6bO+KZONTYyFIQdm/XRp1+tP5L8GY16P7kLLoPCYV2yML2UEvnqM8iNX2L46VzEuLOPPBYOQU0l2tuv67VYg0dg+PFdndYuykAADh9AVpRCeSmyvFTv+xcK6rWG9gx9cIQjA5B6U18wAKMnYLjkWr0ZsrPy1VSgzfsFJCVjmPvbE/Ynk1Ii3/kf5OK39dG9l96AfPdPyBWL9abi62/TJzr+l9dNNR22UYlW36Ti0nup2PROKi691xn1nwv49WSr0Y2YcF63THQqgwG0Z/8fVJQiJs9AOmugtgqcdXrzoCUG8f0fI6ZdenoLw0cier+xhKSOTXmeZuSyj5BL39dHqBYO1Tu05xZAboHeD8vTjDZvDgT8GB58+qSjUqEt2frLq8ilH0CMFYIBxAWX6YvJH6cvmUq02qhEq29Scem9VGx6JxWX3qs3x0Y2N6A9+1/6lB3pWYiMHH0KkfRsxNCRCFv3TAMhA37kl0v0WqeaiiNzrsVa9b5lvlYMD/zmtJqSpaYh31qErDqM4bpbEfkDTri96qOlKIqiKEq3EkmpGB55Qf++C5okT/m4MbGI6ZfD9MuRfh9UHUZWHIKKQ8j6GgzTLz/t/nrCYEDceHv3FPgbUomWoiiKovRRPZlgdXr8ryfQHTAkquXoTt07U5qiKIqiKEofphItRVEURVGUbqISLUVRFEVRlG6iEi1FURRFUZRuohItRVEURVGUbqISLUVRFEVRlG6iEi1FURRFUZRu0mPzaG3dupXXXnsNTdOYPn06V199dU8dWlEURVEUJSp6pEZL0zQWLVrEQw89xPz581m1ahUVFRU9cWhFURRFUZSo6ZFEa//+/WRmZpKRkYHJZGLy5Mls2LChJw6tKIqiKIoSNT2SaLndbux2e/vPdrsdt9vdE4dWFEVRFEWJml611uGSJUtYsmQJAE8++SQOh6Nbj2cymbr9GMrpU3HpvVRseicVl95LxaZ36sm49EiiZbPZcLlc7T+7XC5sNluH7WbMmMGMGTPaf3Y6nd1aLofD0e3HUE6fikvvpWLTO6m49F4qNr1Td8QlOzu709/3SNNhYWEh1dXV1NXVEQ6HWb16NcXFxT1xaEVRFEVRlKjpkRoto9HIbbfdxhNPPIGmaZx//vnk5eX1xKEVRVEURVGipsf6aI0bN45x48b11OEURVEURVGiTs0MryiKoiiK0k1UoqUoiqIoitJNhJRSRrsQiqIoiqIo30V9ukZr7ty50S6C0gkVl95LxaZ3UnHpvVRseqeejEufTrQURVEURVG6k0q0FEVRFEVRuonxkUceeSTahYimAQMGRLsISidUXHovFZveScWl91Kx6Z16Ki6qM7yiKIqiKEo3UU2HiqIoiqIo3aTHZobvTbZu3cprr72GpmlMnz6dq6++OtpF6rOcTicvvvgijY2NCCGYMWMGl156KR6Ph/nz51NfX09aWhr33XcfCQkJ0S5un6NpGnPnzsVmszF37lzq6upYsGABLS0tDBgwgLvuuguTqU9eRqKqtbWVhQsXUl5ejhCCO++8k+zsbHXORNkHH3zA0qVLEUKQl5fH7NmzaWxsVOdMFLz00kts3ryZ5ORknn32WYDj3leklLz22mts2bKFmJgYZs+e3aXNin2uRkvTNBYtWsRDDz3E/PnzWbVqFRUVFdEuVp9lNBq55ZZbmD9/Pk888QSffPIJFRUVvPvuu4wcOZLnn3+ekSNH8u6770a7qH3SRx99RE5OTvvPf/rTn7jssst44YUXiI+PZ+nSpVEsXd/12muvMWbMGBYsWMDTTz9NTk6OOmeizO12s3jxYp588kmeffZZNE1j9erV6pyJkmnTpvHQQw8d87vjnSNbtmyhpqaG559/njvuuIM//OEPXVqWPpdo7d+/n8zMTDIyMjCZTEyePJkNGzZEu1h9VmpqavsnB6vVSk5ODm63mw0bNjB16lQApk6dqmIUBS6Xi82bNzN9+nQApJTs3LmTSZMmAfqFTMWl53m9Xnbv3s0FF1wAgMlkIj4+Xp0zvYCmaQSDQSKRCMFgkJSUFHXORElRUVGHGt3jnSMbN27kvPPOQwjB4MGDaW1tpaGhocvK0ufqL91uN3a7vf1nu93Ovn37olgi5Wt1dXWUlpYycOBAmpqaSE1NBSAlJYWmpqYol67vef3117n55pvx+XwAtLS0EBcXh9FoBMBms+F2u6NZxD6prq6OpKQkXnrpJcrKyhgwYAAzZ85U50yU2Ww2rrjiCu68804sFgujR49mwIAB6pzpRY53jrjdbhwOR/t2drsdt9vdvu031edqtJTeye/38+yzzzJz5kzi4uKOeUwIgRAiSiXrmzZt2kRycrIalt4LRSIRSktLueiii/jtb39LTExMh2ZCdc70PI/Hw4YNG3jxxRd5+eWX8fv9bN26NdrFUo6jJ8+RPlejZbPZcLlc7T+7XC5sNlsUS6SEw2GeffZZzj33XCZOnAhAcnIyDQ0NpKam0tDQQFJSUpRL2beUlJSwceNGtmzZQjAYxOfz8frrr+P1eolEIhiNRtxutzp3osBut2O32xk0aBAAkyZN4t1331XnTJRt376d9PT09td94sSJlJSUqHOmFzneOWKz2XA6ne3bdXVe0OdqtAoLC6murqauro5wOMzq1aspLi6OdrH6LCklCxcuJCcnh8svv7z998XFxaxYsQKAFStWMH78+GgVsU+66aabWLhwIS+++CL33nsvI0aM4O6772b48OGsXbsWgOXLl6tzJwpSUlKw2+1UVVUB+g0+NzdXnTNR5nA42LdvH4FAAClle1zUOdN7HO8cKS4uZuXKlUgp2bt3L3FxcV3WbAh9dMLSzZs388Ybb6BpGueffz7XXntttIvUZ+3Zs4df/epX5Ofnt1fj/vCHP2TQoEHMnz8fp9OphqpH2c6dO3n//feZO3cutbW1LFiwAI/HQ//+/bnrrrswm83RLmKfc+jQIRYuXEg4HCY9PZ3Zs2cjpVTnTJS99dZbrF69GqPRSEFBAT/96U9xu93qnImCBQsWsGvXLlpaWkhOTuaGG25g/PjxnZ4jUkoWLVrEV199hcViYfbs2RQWFnZZWfpkoqUoiqIoitIT+lzToaIoiqIoSk9RiZaiKIqiKEo3UYmWoiiKoihKN1GJlqIoiqIoSjdRiZaiKIqiKEo3UYmWoih92g033EBNTU20i6EoyndUn5sZXlGU3utnP/sZjY2NGAxHPgNOmzaNWbNmRbFUnfvkk09wuVzcdNNNPPzww9x2223069cv2sVSFKWXUYmWoii9yi9/+UtGjRoV7WKc1MGDBxk3bhyaplFZWUlubm60i6QoSi+kEi1FUb4Vli9fzueff05BQQErV64kNTWVWbNmMXLkSADcbjevvvoqe/bsISEhgauuuooZM2YAoGka7777LsuWLaOpqYmsrCzmzJmDw+EAYNu2bfzmN7+hubmZKVOmMGvWrJMuOHvw4EGuu+46qqqqSEtLw2g0du8LoCjKt5JKtBRF+dbYt28fEydOZNGiRaxfv55nnnmGF198kYSEBJ577jny8vJ4+eWXqaqq4rHHHiMzM5MRI0bwwQcfsGrVKh588EGysrIoKysjJiamfb+bN29m3rx5+Hw+fvnLX1JcXMyYMWM6HD8UCnH77bcjpcTv9zNnzhzC4TCapjFz5kyuvPJKtaSXoijHUImWoii9ytNPP31M7dDNN9/cXjOVnJzMZZddhhCCyZMn8/7777N582aKiorYs2cPc+fOxWKxUFBQwPTp01mxYgUjRozg888/5+abbyY7OxuAgoKCY4559dVXEx8fT3x8PMOHD+fQoUOdJlpms5nXX3+dzz//nPLycmbOnMnjjz/OjTfeyMCBA7vvRVEU5VtLJVqKovQqc+bMOW4fLZvNdkyTXlpaGm63m4aGBhISErBare2PORwODhw4AIDL5SIjI+O4x0xJSWn/PiYmBr/f3+l2CxYsYOvWrQQCAcxmM8uWLcPv97N//36ysrKYN2/eaT1XRVG++1SipSjKt4bb7UZK2Z5sOZ1OiouLSU1NxePx4PP52pMtp9OJzWYDwG63U1tbS35+/jc6/r333oumadxxxx288sorbNq0iTVr1nD33Xd/syemKMp3lppHS1GUb42mpiYWL15MOBxmzZo1VFZWMnbsWBwOB0OGDOHNN98kGAxSVlbGsmXLOPfccwGYPn06f/3rX6murkZKSVlZGS0tLWdUhsrKSjIyMjAYDJSWllJYWNiVT1FRlO8YVaOlKEqv8tRTTx0zj9aoUaOYM2cOAIMGDaK6uppZs2aRkpLC/fffT2JiIgD33HMPr776Kj/5yU9ISEjg+uuvb2+CvPzyywmFQjz++OO0tLSQk5PDAw88cEblO3jwIP3792///qqrrvomT1dRlO84IaWU0S6EoijKyXw9vcNjjz0W7aIoiqKcMtV0qCiKoiiK0k1UoqUoiqIoitJNVNOhoiiKoihKN1E1WoqiKIqiKN1EJVqKoiiKoijdRCVaiqIoiqIo3UQlWoqiKIqiKN1EJVqKoiiKoijdRCVaiqIoiqIo3eT/A/eHrp8sGTcYAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bYQI9tEnVCf"
      },
      "source": [
        "## Model with Dropout Layer and Regularization\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "xLMplWEInVCf"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ib6jE9DwnVCg"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dRmURCLEO7js"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oyk3dUbfHKS1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Adding dropout and regularization"
      ],
      "metadata": {
        "id": "bGyDDQl8SxWH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#kernel_regularizer=tf.keras.regularizers.l2(0.1)\n",
        "\n",
        "\n",
        "\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(80, input_dim=60, activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.1)),\n",
        "    \n",
        "    keras.layers.Dense(30, activation='relu'),\n",
        "    \n",
        "    keras.layers.Dense(15, activation='relu'),\n",
        "    keras.layers.Dropout(0.1),\n",
        "    keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "H = model.fit(X_train, y_train, epochs=100, batch_size=8, validation_split=0.3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6XUc1rITGTT",
        "outputId": "541cfbf6-b134-4ab8-c064-b175be0220d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "14/14 [==============================] - 1s 19ms/step - loss: 6.9632 - accuracy: 0.4862 - val_loss: 6.2737 - val_accuracy: 0.4255\n",
            "Epoch 2/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 5.7688 - accuracy: 0.4954 - val_loss: 5.1909 - val_accuracy: 0.4681\n",
            "Epoch 3/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 4.7729 - accuracy: 0.5505 - val_loss: 4.2967 - val_accuracy: 0.5319\n",
            "Epoch 4/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 3.9521 - accuracy: 0.6422 - val_loss: 3.5652 - val_accuracy: 0.5106\n",
            "Epoch 5/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 3.2866 - accuracy: 0.6606 - val_loss: 2.9713 - val_accuracy: 0.5957\n",
            "Epoch 6/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 2.7392 - accuracy: 0.6972 - val_loss: 2.4898 - val_accuracy: 0.6170\n",
            "Epoch 7/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 2.2956 - accuracy: 0.7431 - val_loss: 2.0996 - val_accuracy: 0.7872\n",
            "Epoch 8/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 1.9531 - accuracy: 0.6972 - val_loss: 1.7905 - val_accuracy: 0.5957\n",
            "Epoch 9/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 1.6613 - accuracy: 0.6972 - val_loss: 1.5339 - val_accuracy: 0.8085\n",
            "Epoch 10/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 1.4334 - accuracy: 0.7156 - val_loss: 1.3334 - val_accuracy: 0.7234\n",
            "Epoch 11/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 1.2500 - accuracy: 0.7431 - val_loss: 1.1684 - val_accuracy: 0.7234\n",
            "Epoch 12/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 1.0967 - accuracy: 0.7615 - val_loss: 1.0300 - val_accuracy: 0.7872\n",
            "Epoch 13/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.9808 - accuracy: 0.7798 - val_loss: 0.9295 - val_accuracy: 0.8298\n",
            "Epoch 14/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.8815 - accuracy: 0.7615 - val_loss: 0.8325 - val_accuracy: 0.8723\n",
            "Epoch 15/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.8405 - accuracy: 0.6881 - val_loss: 0.7570 - val_accuracy: 0.7872\n",
            "Epoch 16/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.8124 - accuracy: 0.6514 - val_loss: 0.7611 - val_accuracy: 0.6596\n",
            "Epoch 17/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.7021 - accuracy: 0.7798 - val_loss: 0.7622 - val_accuracy: 0.6809\n",
            "Epoch 18/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.6636 - accuracy: 0.7523 - val_loss: 0.6479 - val_accuracy: 0.7660\n",
            "Epoch 19/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.6447 - accuracy: 0.7064 - val_loss: 0.6318 - val_accuracy: 0.8511\n",
            "Epoch 20/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.6045 - accuracy: 0.8165 - val_loss: 0.5965 - val_accuracy: 0.8511\n",
            "Epoch 21/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5872 - accuracy: 0.7890 - val_loss: 0.6001 - val_accuracy: 0.7660\n",
            "Epoch 22/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.5982 - accuracy: 0.7706 - val_loss: 0.6052 - val_accuracy: 0.7447\n",
            "Epoch 23/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.5240 - accuracy: 0.8532 - val_loss: 0.5720 - val_accuracy: 0.8298\n",
            "Epoch 24/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.5111 - accuracy: 0.8440 - val_loss: 0.5688 - val_accuracy: 0.8298\n",
            "Epoch 25/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.4997 - accuracy: 0.8624 - val_loss: 0.5368 - val_accuracy: 0.8511\n",
            "Epoch 26/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5880 - accuracy: 0.7523 - val_loss: 0.5880 - val_accuracy: 0.8298\n",
            "Epoch 27/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.6247 - accuracy: 0.7248 - val_loss: 0.5703 - val_accuracy: 0.8511\n",
            "Epoch 28/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.5514 - accuracy: 0.7798 - val_loss: 0.6361 - val_accuracy: 0.7660\n",
            "Epoch 29/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.5348 - accuracy: 0.8165 - val_loss: 0.6395 - val_accuracy: 0.7234\n",
            "Epoch 30/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.5333 - accuracy: 0.7523 - val_loss: 0.6031 - val_accuracy: 0.8298\n",
            "Epoch 31/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.5073 - accuracy: 0.8349 - val_loss: 0.5987 - val_accuracy: 0.8298\n",
            "Epoch 32/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.5113 - accuracy: 0.8165 - val_loss: 0.7127 - val_accuracy: 0.7021\n",
            "Epoch 33/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.5450 - accuracy: 0.7615 - val_loss: 0.5972 - val_accuracy: 0.8298\n",
            "Epoch 34/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.5363 - accuracy: 0.7798 - val_loss: 0.5383 - val_accuracy: 0.8298\n",
            "Epoch 35/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4373 - accuracy: 0.8991 - val_loss: 0.5159 - val_accuracy: 0.8511\n",
            "Epoch 36/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.4274 - accuracy: 0.8807 - val_loss: 0.5229 - val_accuracy: 0.8298\n",
            "Epoch 37/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.4458 - accuracy: 0.8807 - val_loss: 0.5244 - val_accuracy: 0.8298\n",
            "Epoch 38/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.4082 - accuracy: 0.8899 - val_loss: 0.5210 - val_accuracy: 0.8298\n",
            "Epoch 39/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4295 - accuracy: 0.8440 - val_loss: 0.5180 - val_accuracy: 0.8511\n",
            "Epoch 40/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4076 - accuracy: 0.8991 - val_loss: 0.5237 - val_accuracy: 0.8511\n",
            "Epoch 41/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3781 - accuracy: 0.9358 - val_loss: 0.5548 - val_accuracy: 0.8085\n",
            "Epoch 42/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4032 - accuracy: 0.8991 - val_loss: 0.5386 - val_accuracy: 0.8298\n",
            "Epoch 43/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.4082 - accuracy: 0.8807 - val_loss: 0.5255 - val_accuracy: 0.8085\n",
            "Epoch 44/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3586 - accuracy: 0.9083 - val_loss: 0.5249 - val_accuracy: 0.8511\n",
            "Epoch 45/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.3874 - accuracy: 0.8807 - val_loss: 0.5120 - val_accuracy: 0.8511\n",
            "Epoch 46/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.4358 - accuracy: 0.8349 - val_loss: 0.5763 - val_accuracy: 0.8085\n",
            "Epoch 47/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3599 - accuracy: 0.9266 - val_loss: 0.5142 - val_accuracy: 0.8298\n",
            "Epoch 48/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3422 - accuracy: 0.9174 - val_loss: 0.5227 - val_accuracy: 0.8298\n",
            "Epoch 49/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3653 - accuracy: 0.9174 - val_loss: 0.5831 - val_accuracy: 0.8298\n",
            "Epoch 50/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3611 - accuracy: 0.8899 - val_loss: 0.5462 - val_accuracy: 0.8085\n",
            "Epoch 51/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3667 - accuracy: 0.9266 - val_loss: 0.5950 - val_accuracy: 0.8298\n",
            "Epoch 52/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.5105 - accuracy: 0.8165 - val_loss: 0.5114 - val_accuracy: 0.8511\n",
            "Epoch 53/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3423 - accuracy: 0.9266 - val_loss: 0.5244 - val_accuracy: 0.8298\n",
            "Epoch 54/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3815 - accuracy: 0.8716 - val_loss: 0.5567 - val_accuracy: 0.7872\n",
            "Epoch 55/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.3287 - accuracy: 0.9266 - val_loss: 0.5441 - val_accuracy: 0.8085\n",
            "Epoch 56/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.3229 - accuracy: 0.9358 - val_loss: 0.5395 - val_accuracy: 0.8511\n",
            "Epoch 57/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3317 - accuracy: 0.9083 - val_loss: 0.5309 - val_accuracy: 0.8298\n",
            "Epoch 58/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.3580 - accuracy: 0.8807 - val_loss: 0.5309 - val_accuracy: 0.8511\n",
            "Epoch 59/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3476 - accuracy: 0.9083 - val_loss: 0.5900 - val_accuracy: 0.8298\n",
            "Epoch 60/100\n",
            "14/14 [==============================] - 0s 12ms/step - loss: 0.3399 - accuracy: 0.8991 - val_loss: 0.5392 - val_accuracy: 0.8511\n",
            "Epoch 61/100\n",
            "14/14 [==============================] - 0s 11ms/step - loss: 0.3094 - accuracy: 0.9358 - val_loss: 0.5693 - val_accuracy: 0.8298\n",
            "Epoch 62/100\n",
            "14/14 [==============================] - 0s 10ms/step - loss: 0.4411 - accuracy: 0.8073 - val_loss: 0.5420 - val_accuracy: 0.8298\n",
            "Epoch 63/100\n",
            "14/14 [==============================] - 0s 17ms/step - loss: 0.3488 - accuracy: 0.9083 - val_loss: 0.7303 - val_accuracy: 0.8085\n",
            "Epoch 64/100\n",
            "14/14 [==============================] - 0s 12ms/step - loss: 0.4917 - accuracy: 0.7982 - val_loss: 0.7241 - val_accuracy: 0.7660\n",
            "Epoch 65/100\n",
            "14/14 [==============================] - 0s 10ms/step - loss: 0.4084 - accuracy: 0.8624 - val_loss: 0.5179 - val_accuracy: 0.8511\n",
            "Epoch 66/100\n",
            "14/14 [==============================] - 0s 12ms/step - loss: 0.3145 - accuracy: 0.9358 - val_loss: 0.5678 - val_accuracy: 0.8085\n",
            "Epoch 67/100\n",
            "14/14 [==============================] - 0s 10ms/step - loss: 0.2879 - accuracy: 0.9725 - val_loss: 0.5641 - val_accuracy: 0.8511\n",
            "Epoch 68/100\n",
            "14/14 [==============================] - 0s 15ms/step - loss: 0.2994 - accuracy: 0.9358 - val_loss: 0.5634 - val_accuracy: 0.8298\n",
            "Epoch 69/100\n",
            "14/14 [==============================] - 0s 10ms/step - loss: 0.3618 - accuracy: 0.8899 - val_loss: 0.5626 - val_accuracy: 0.8298\n",
            "Epoch 70/100\n",
            "14/14 [==============================] - 0s 9ms/step - loss: 0.2927 - accuracy: 0.9358 - val_loss: 0.5918 - val_accuracy: 0.8298\n",
            "Epoch 71/100\n",
            "14/14 [==============================] - 0s 13ms/step - loss: 0.2951 - accuracy: 0.9450 - val_loss: 0.5835 - val_accuracy: 0.8298\n",
            "Epoch 72/100\n",
            "14/14 [==============================] - 0s 10ms/step - loss: 0.2631 - accuracy: 0.9541 - val_loss: 0.5972 - val_accuracy: 0.8298\n",
            "Epoch 73/100\n",
            "14/14 [==============================] - 0s 11ms/step - loss: 0.2598 - accuracy: 0.9633 - val_loss: 0.6042 - val_accuracy: 0.8298\n",
            "Epoch 74/100\n",
            "14/14 [==============================] - 0s 10ms/step - loss: 0.3098 - accuracy: 0.9174 - val_loss: 0.6108 - val_accuracy: 0.8298\n",
            "Epoch 75/100\n",
            "14/14 [==============================] - 0s 7ms/step - loss: 0.2952 - accuracy: 0.9358 - val_loss: 0.5733 - val_accuracy: 0.8511\n",
            "Epoch 76/100\n",
            "14/14 [==============================] - 0s 12ms/step - loss: 0.2971 - accuracy: 0.9450 - val_loss: 0.5942 - val_accuracy: 0.8511\n",
            "Epoch 77/100\n",
            "14/14 [==============================] - 0s 11ms/step - loss: 0.2672 - accuracy: 0.9725 - val_loss: 0.5700 - val_accuracy: 0.8511\n",
            "Epoch 78/100\n",
            "14/14 [==============================] - 0s 10ms/step - loss: 0.2869 - accuracy: 0.9541 - val_loss: 0.6057 - val_accuracy: 0.8511\n",
            "Epoch 79/100\n",
            "14/14 [==============================] - 0s 11ms/step - loss: 0.2722 - accuracy: 0.9266 - val_loss: 0.6609 - val_accuracy: 0.8298\n",
            "Epoch 80/100\n",
            "14/14 [==============================] - 0s 13ms/step - loss: 0.4002 - accuracy: 0.8257 - val_loss: 0.6244 - val_accuracy: 0.8298\n",
            "Epoch 81/100\n",
            "14/14 [==============================] - 0s 11ms/step - loss: 0.3012 - accuracy: 0.9174 - val_loss: 0.5806 - val_accuracy: 0.8511\n",
            "Epoch 82/100\n",
            "14/14 [==============================] - 0s 12ms/step - loss: 0.3334 - accuracy: 0.8807 - val_loss: 0.6909 - val_accuracy: 0.7872\n",
            "Epoch 83/100\n",
            "14/14 [==============================] - 0s 14ms/step - loss: 0.3614 - accuracy: 0.8807 - val_loss: 0.6427 - val_accuracy: 0.7872\n",
            "Epoch 84/100\n",
            "14/14 [==============================] - 0s 9ms/step - loss: 0.4342 - accuracy: 0.8257 - val_loss: 0.5699 - val_accuracy: 0.8511\n",
            "Epoch 85/100\n",
            "14/14 [==============================] - 0s 9ms/step - loss: 0.3309 - accuracy: 0.8899 - val_loss: 0.5530 - val_accuracy: 0.8298\n",
            "Epoch 86/100\n",
            "14/14 [==============================] - 0s 11ms/step - loss: 0.2914 - accuracy: 0.9358 - val_loss: 0.5835 - val_accuracy: 0.8511\n",
            "Epoch 87/100\n",
            "14/14 [==============================] - 0s 9ms/step - loss: 0.2607 - accuracy: 0.9725 - val_loss: 0.5803 - val_accuracy: 0.8511\n",
            "Epoch 88/100\n",
            "14/14 [==============================] - 0s 12ms/step - loss: 0.2482 - accuracy: 0.9541 - val_loss: 0.6356 - val_accuracy: 0.8085\n",
            "Epoch 89/100\n",
            "14/14 [==============================] - 0s 10ms/step - loss: 0.3678 - accuracy: 0.8532 - val_loss: 0.6689 - val_accuracy: 0.8298\n",
            "Epoch 90/100\n",
            "14/14 [==============================] - 0s 9ms/step - loss: 0.3012 - accuracy: 0.9174 - val_loss: 0.5753 - val_accuracy: 0.8511\n",
            "Epoch 91/100\n",
            "14/14 [==============================] - 0s 12ms/step - loss: 0.2521 - accuracy: 0.9633 - val_loss: 0.5857 - val_accuracy: 0.8511\n",
            "Epoch 92/100\n",
            "14/14 [==============================] - 0s 10ms/step - loss: 0.2331 - accuracy: 0.9633 - val_loss: 0.6237 - val_accuracy: 0.8298\n",
            "Epoch 93/100\n",
            "14/14 [==============================] - 0s 20ms/step - loss: 0.2064 - accuracy: 0.9817 - val_loss: 0.6278 - val_accuracy: 0.8511\n",
            "Epoch 94/100\n",
            "14/14 [==============================] - 0s 20ms/step - loss: 0.2131 - accuracy: 0.9817 - val_loss: 0.6442 - val_accuracy: 0.8511\n",
            "Epoch 95/100\n",
            "14/14 [==============================] - 0s 16ms/step - loss: 0.1984 - accuracy: 0.9908 - val_loss: 0.6584 - val_accuracy: 0.8511\n",
            "Epoch 96/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2004 - accuracy: 0.9817 - val_loss: 0.6561 - val_accuracy: 0.8511\n",
            "Epoch 97/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1933 - accuracy: 0.9908 - val_loss: 0.6467 - val_accuracy: 0.8511\n",
            "Epoch 98/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.2037 - accuracy: 0.9908 - val_loss: 0.6346 - val_accuracy: 0.8511\n",
            "Epoch 99/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.1933 - accuracy: 0.9725 - val_loss: 0.6557 - val_accuracy: 0.8511\n",
            "Epoch 100/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.2216 - accuracy: 0.9541 - val_loss: 0.6509 - val_accuracy: 0.8298\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, acc = model.evaluate(X_test, y_test)\n",
        "print('Test Accuracy: ', acc*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCEbtP13TPIc",
        "outputId": "3503d5bf-b979-4aee-da86-c918aed87fe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 6ms/step - loss: 0.7192 - accuracy: 0.7692\n",
            "Test Accuracy:  76.92307829856873\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, acc = model.evaluate(X_train, y_train)\n",
        "print('Test Accuracy: ', acc*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uRjmzT3sTRv8",
        "outputId": "35a3dffe-d69c-4fcd-9edb-00613cf7b242"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 0s 3ms/step - loss: 0.3445 - accuracy: 0.9295\n",
            "Test Accuracy:  92.94871687889099\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "epochs = np.arange(0, len(H.history[\"loss\"]))\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.style.use(\"ggplot\")\n",
        "\n",
        "\n",
        "# plot training and validation loss\n",
        "plt.style.use(\"ggplot\")\n",
        "plt.plot(epochs, H.history[\"loss\"], label=\"train_loss\")\n",
        "plt.plot(epochs, H.history[\"val_loss\"], label=\"val_loss\")\n",
        "plt.title(\"Training Vs Validation Loss\")\n",
        "plt.xlabel(\"Epoch #\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "C3ta_WAOTUhX",
        "outputId": "c7807d8d-df29-4ae3-f4fb-0cf5ba43d965"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5e868684d0>"
            ]
          },
          "metadata": {},
          "execution_count": 201
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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75HK5dPvtt+tf//qXXC6Xjj76aN188816+eWXdc8998jhcCgzM1Nz587t8W9D0AIAADF14403auXKlXrjjTf07rvv6oc//KHefPNNDRo0SJI0e/Zs5eTkqLGxUd/97nd16qmnKjc3t8NnlJWV6f7779esWbM0ffp0vfbaazrrrLN2225TU5OuueYa/e1vf1NxcbGuuuoqPfXUUzrvvPM0b948LVy4UJZltU9P3nvvvXrmmWfUv3//vZqy7ExcglZ5ebnuueee9sdbtmzRueeeq+9+97vxaB4AALTa3chTvIwfP749ZEnSY489pnnz5kmKZoaysrKdgtbAgQM1duxYSdK4ceO0YcOGPbazZs0aDRo0SMXFxZKkc845R08++aQuu+wypaam6rrrrlNJSYlKSkokSQcffLCuueYaTZkyRaecckqvfNe4BK1AIKBZs2ZJiu7lMX36dE2aNCkeTQMAgASTlpbW/vd3331Xb7/9tl5++WV5vV6dffbZne7anpqa2v53p9OppqamvW7f5XLp1Vdf1aJFi/Tqq6/q8ccf1/PPP68777xTixcv1vz583XKKado3rx5OwW+brfVo3fvhWXLlqmwsFB5eXnxbhoAANggPT1ddXV1nT5XW1urrKwseb1erV69WosXL+61douLi7VhwwaVlZVp6NChmjNnjg477DDV19ertrZWkydP1iGHHKLDDz9ckrR27VpNmDBBEyZM0IIFC1ReXt73gtY777yjI444It7NAgAAm+Tm5uqQQw7R8ccfL4/HI7/f3/7cscceq6efflrHHHOMiouLNWHChF5r1+Px6O6779b06dPbF8NfdNFFqqur09SpUxUMBmWM0YwZMyRJt956q8rKymSM0ZFHHqkxY8b0uAbLGGN6/CldFAqFNH36dM2ePVvZ2dk7PV9aWqrS0lJJ0syZM9Xc3ByzWkwwKKeJKOLp2llFiB+Xy7XHW35hD/omMdEviStR+mbz5s0dpt7QM8FgUAUFBR2uud3uTl8b1xGtjz/+WEOHDu00ZEnqsCBNkiorK2NWS3jG/1PqoGEKXXJtzNrA3vH7/THte+w9+iYx0S+JK1H6JhgM7vUhyvuingbgYDC4U78GAoHO29rrVvZCQk0bpmcoUts7t24CAID4u/HGG/XBBx90uHbppZfqvPPOs6mincUtaDU1NWnp0qW6/PLL49Xk7qVnymzbYncVAABgL91+++12l7BHcTuCx+Px6LHHHutwS6edrAyfIrU1dpcBAAD2Ycl71mG6T5HaasXxXgAAAJBkkjdoZfikUIvUvPOmaAAAAL0heYNWui/637pae+sAAAD7rKQNWlZb0KpnnRYAAIlkxIgRu3xuw4YNOv744+NYTc8kbdBSBiNaAAAgtuJ+BE/CSM+UJJn6Wlk2lwIAQLz8+cPNKqva+wOZOzM0x6NLDy7Y5fO33367AoGApk2bJkmaPXu2nE6n3n33XVVXVysUCukXv/iFTjrppG6129TUpBtuuEFLly6V0+nUjBkzdMQRR2jlypW69tpr1dzcLGOMHn74YRUWFmr69OnauHGjIpGIrr76an3ve9/rydfukuQNWm0jWvWMaAEAEEunn366ZsyY0R60Xn75ZT3zzDO65JJL5PP5tG3bNk2ZMkUnnniiLKvrwx9PPPGELMvS/PnztXr1an3/+9/X22+/raefflqXXHKJzjzzTDU3NyscDuvNN99UYWGhnn76ablcLm3bti1G37aj5A1a6RnR/zJ1CABIIrsbeYqVsWPHqrKyUps2bdLWrVuVlZWl/Px8/fa3v9V///tfWZalTZs2qaKiQvn5+V3+3A8++EA/+tGPJEnDhw9XUVGRvvzyS02cOFH33XefNm7cqFNOOUXDhg3TqFGj9L//+7+67bbbdNJJJ+nggw+O1dftIGnXaFmuFFmeNEa0AACIg9NOO02vvvqqXnrpJZ1++umaO3eutm7dqnnz5umNN96Q3+9XMNg7Wy6dccYZevzxx+XxeHTRRRdp0aJFKi4u1uuvv65Ro0Zp5syZuueee3qlrT1J2qAlSZYvkxEtAADi4PTTT9eLL76oV199Vaeddppqa2vl9/uVkpKid955R1999VW3P3PSpEn6xz/+IUlas2aNvv76axUXF2vdunUaPHiwLrnkEp100kn6/PPPtWnTJnm9Xp111ln6yU9+omXLlvX2V+xU8k4dSnL4shRiRAsAgJgbOXKk6uvrVVhYqIKCAp155pmaOnWqJk+erHHjxmn48OHd/sypU6fqhhtu0OTJk+V0OnXPPfcoNTVVL7/8subMmSOXy6X8/Hz99Kc/1SeffKJbb71VlmXJ7XbH7ZxEyyTwGTTl5eUx/XznH29Rc22NnDfMimk76B6/36/Kykq7y0An6JvERL8krkTpm4aGhoQ5azgRuFwuhUKhvX5/Z79nIBDo9LVMHTJ1CAAAYiS5pw4zslgMDwBAAvr888911VVXdbiWmpqqV155xaaK9k5yBy1fltRQJxMJy3I47S4HAAC02n///fXGG2/YXUaPJfXUocOXKRkjNdTbXQoAADGTwMux+6Tu/J5JHbQsX1b0L/V19hYCAEAMORyOHi3+xjdCoZAcjq7HJ6YOJamuRiro/G4BAAD6Oo/Ho6amJgWDwW4dcbOvSk1N3avNUY0xcjgc8ng8XX5Pkget6MHSLIgHAOzLLMuS1+u1u4yEEc9tN5g6lGTY4gEAAMRAUgctRrQAAEAsJXXQstIyJMtB0AIAADGR3EHL4ZDSMwhaAAAgJpI6aEmS0n0cwwMAAGKCoJXhk2FECwAAxABBK90X3UcLAACglyV90LLSfewMDwAAYiLpg5YyfCyGBwAAMUHQSvdJwSaZlha7KwEAAPsYgla6L/rfetZpAQCA3pX0QcvKaA1abPEAAAB6WdIHrW9GtFgQDwAAehdBK6PtvEOmDgEAQO8iaKVnSJIMU4cAAKCXEbTS20a0CFoAAKB3JX3QslJTpRQ3i+EBAECvc9ldgF1mLfpaA3JrdMHozOiCeEa0AABAL0vaoFVRH1JTpF5SJgdLAwCAmEjaqcMsj1PbG1t3g0/3MXUIAAB6XdIGrcxUp6oaQ9EHTB0CAIAYSOqgVd3YImNMdHd4ghYAAOhlSRu0sjxOhSJGDS2R9hEtY4zdZQEAgH1I0gatzNTofQA1wbCU4ZPCYamp0eaqAADAviSJg5ZTklTdFP5m09I6juEBAAC9J2mDVpYnGrRqgiFZrcfwsE4LAAD0pqQNWm0jWu1Th5JUX2djRQAAYF+TtEEryxNdo7Xj1KFh6hAAAPSipA1aqU5LbqfjWyNaTB0CAIDek7RBy7Is5XhTVBMMSWmta7TYHR4AAPSiuJ11WF9frwcffFAbNmyQZVn68Y9/rP322y9ezXcqO82l6qawLKdT8qYzogUAAHpV3ILW448/rvHjx+u6665TKBRSMBiMV9O7lO1N0ba6pugDdocHAAC9LC5Thw0NDfr88891/PHHS5JcLpfS09Pj0fRuZXlSomu0JCndJ0PQAgAAvSguI1pbtmxRZmamHnjgAa1bt07Dhg3TtGnT5PF44tH8LmV7U6J3HUrRES3WaAEAgF4Ul6AVDodVVlamiy++WCNGjNDjjz+uF154Qeeff36H15WWlqq0tFSSNHPmTPn9/pjW1S8jqKZQRL7sXDXl+tVSsSnmbWLPXC4X/ZCg6JvERL8kLvomMcWzX+IStPr166d+/fppxIgRkqTDDjtML7zwwk6vKykpUUlJSfvjysrKmNbVtmnpl19vVj+XW6amOuZtYs/8fj/9kKDom8REvyQu+iYxxaJfAoFAp9fjskYrOztb/fr1U3l5uSRp2bJlKioqikfTu9W2aWltMCyl+6TGeplw2OaqAADAviJudx1efPHFuu+++xQKhZSfn6+f/OQn8Wp6l7K9KZKk6h03LW2ok3xZNlYFAAD2FXELWkOGDNHMmTPj1VyXZKe1Bq2mUHRES4ouiCdoAQCAXpC0O8NLUk7riFZNMCwrI3reoeo57xAAAPSOpA5aGakuOSyppikspXMMDwAA6F1JHbQcliVfqjO6aWnr1KGpr7O5KgAAsK9I6qAlSVmpTlUHQxJThwAAoJclfdDK9LiiU4cer+R0MnUIAAB6DUGrderQsqzo9CHnHQIAgF6S9EErOnW4w8HSjGgBAIBekvRBK9PjVF0wrHDEMKIFAAB6VdIHraxUl4yk2uZwdEF8HYvhAQBA70j6oOVrPVi6JhiWlZkl1VbbXBEAANhXJH3QyvK0Bq2mcPTondoamQgHSwMAgJ4jaLWOaFUHQ1JmtmQiEpuWAgCAXpD0QSvTEz1XOzqilR29WLPdxooAAMC+IumDls+94xotghYAAOg9SR+0UpyW0lMc0b20WoOWIWgBAIBekPRBS4rupVXTFJIys6IXuPMQAAD0AoKWpMxUl2qCYSktI3reISNaAACgFxC09K3zDn1ZBC0AANArCFqK7qVV3dS6d1ZmtgxThwAAoBcQtNQ2ohWSMYYRLQAA0GsIWoqOaIUiUmMoEt3igREtAADQCwhaii6GlxSdPvRlSzXbo6NbAAAAPUDQUnTqUIpuWqrMbKmlWQo22lwVAADo6wha+uZg6eqmUPumpazTAgAAPUXQUscRLcvXumkpQQsAAPQQQUvfrNGqaQrvMKLFgngAANAzBC1JHpclt9NqPe8wOqLFeYcAAKCnCFqSLMtq30tLGZx3CAAAegdBq1WWx6maprAsl0tK97FGCwAA9BhBq5Uv1RWdOpRaj+EhaAEAgJ4haLXKaj1YWhLH8AAAgF5B0GqVucPB0hzDAwAAegNBq1VWqlNNoYiaw5HoFg+MaAEAgB4iaLVq30srGI5OHTbUy7S02FwVAADoywharTJbj+HpsGkp04cAAKAHCFqtslqP4akOhmVltu2lxfQhAADYewStVt+MaIUkH8fwAACAniNoteqwRqt16pBjeAAAQE8QtFpluB1yWIpu8dC+RougBQAA9h5Bq5XDsuRr3bTUSvVI7lS2eAAAAD1C0NpBVtvB0hJ7aQEAgB4jaO0gM/Wb3eGj5x2yGB4AAOw9gtYOsjwubW/ivEMAANA7CFo7yPG6VNUYnTrkvEMAANBTBK0d5HhdagxF1BSKRPfSqq2WiUTsLgsAAPRRBK0d5Hqje2lVNYaii+EjEam+zuaqAABAX0XQ2kFb0NrWGJLajuFhnRYAANhLBK0d5OwwomW1bVpaU2VjRQAAoC8jaO1gx6AlX3REiy0eAADA3iJo7cDndsjlsFqnDttGtJg6BAAAe8cVr4auvPJKeTweORwOOZ1OzZw5M15Nd5llWcrxOKNBKy1DcjrZ4gEAAOy1uAUtSZoxY4YyMzPj2WS3te2lZTkcUgablgIAgL3H1OG37LhpqTKzWKMFAAD2WlxHtG677TZJ0gknnKCSkpKdni8tLVVpaakkaebMmfL7/TGtx+Vy7dTGgNxqfV5ZIb/fr6p+eYrU16lfjOtAR531CxIDfZOY6JfERd8kpnj2S9yC1i233KLc3FxVV1fr1ltvVSAQ0OjRozu8pqSkpEMAq6ysjGlNfr9/pza8alFNU0gbN2+R05Mus2FtzOtAR531CxIDfZOY6JfERd8kplj0SyAQ6PR63KYOc3NzJUlZWVk65JBDtHr16ng13S3fbPEQjm5aWrtdxhibqwIAAH1RXIJWU1OTGhsb2/++dOlSDRo0KB5Nd1t70Gpq3eKhuVkKNtpcFQAA6IviMnVYXV2tu+66S5IUDod15JFHavz48fFouts6HMPjazuGp1rypNlYFQAA6IviErQKCgo0a9aseDTVY20jWtsaosfwGCm6xUN+f1vrAgAAfQ/bO3xLZqpTDqv1GJ623eHZ4gEAAOwFgta3OB2Wsjyu6BotXzRoGTYtBQAAe4Gg1Ynctk1Lfa272NcStAAAQPcRtDqR642ed2i5UqJnHjKiBQAA9gJBqxMdj+HJZuoQAADsFYJWJ3K8LlU3hRWOmNZNS1kMDwAAuo+g1Ykcj0tG0vamkCxfNlOHAABgrxC0OtFh09JMghYAANg7BK1OfHPeYUjKzpUa6mWCQZurAgAAfQ1BqxMdDpbO7he9uH2rjRUBACZqz0kAACAASURBVIC+iKDViWzPNyNaVg5BCwAA7B2CVidSnJayUqN7aak1aJmqSpurAgAAfQ1BaxdyvK3H8LRNHVZts7cgAADQ5xC0dqFt01LL45W86UwdAgCAbiNo7UKO16VtDa27w2fnMnUIAAC6jaC1C7lel7Y3hRQxJrpOaztThwAAoHsIWruQ43UqbKSaYDh652EVU4cAAKB7CFq70HHT0n5SdZVMOGxzVQAAoC8haO1C7g57aSnHL5kIR/EAAIBuIWjtQm7aN+cdWuwODwAA9gJBaxc6TB3m5EYvcuchAADoBoLWLridDqW7Hd9MHUoybFoKAAC6gaC1GzkeV/QYnoxMyeViRAsAAHQLQWs3cr0ubWsMy7IsKSuXNVoAAKBbCFq70XYMT/SBX4ZNSwEAQDcQtHajLWgZY1o3LWXqEAAAdB1BazdyvS61RIzqmyNSdnTq0Bhjd1kAAKCPIGjtRtsWD9uaWu88bG6WGuptrgoAAPQVBK3dyP32MTwS04cAAKDLCFq70T6i1RCKrtGSuPMQAAB0GUFrN3K8Tkltu8NHg5apImgBAICuIWjtRlqKUx6XFV2jlZUTvUjQAgAAXUTQ2oO2LR4sV4qUmc3UIQAA6DKC1h7keHbYtDS7H1OHAACgywhae9Bxd/h+jGgBAIAuI2jtQW6aS1sbdtgdnqAFAAC6iKC1B3lpKQqGjWqbI9G9tOpqZZqDdpcFAAD6AILWHuSnp0iSKupbvtm0lMOlAQBAFxC09iBvh6DFpqUAAKA7CFp7kJce3R2+or6FTUsBAEC3ELT2IDPVKbfT0pYdghYjWgAAoCsIWntgWZby01NUUR+S5UmTPF52hwcAAF1C0OqCvPSU6NShxKalAACgywhaXZCX7vomaLGXFgAA6CKCVhfkpaeoOhhWMBSRlU3QAgAAXUPQ6oL2vbQaWhfEb98mEwnbXBUAAEh0BK0uyEtr20srFA1akYhUU21zVQAAINERtLqgw6albbvDsyAeAADsAUGrC/qlueSwOm5ayjotAACwJ3ENWpFIRL/4xS80c+bMeDbbY06HpVyvq8OmpYagBQAA9iCuQeu1117TgAED4tlkr8lv20srI0tyuqSqSrtLAgAACS5uQWvr1q1avHixJk+eHK8me1XbpqWWwyFl50pV2+wuCQAAJLi4Ba0nnnhCF154oSzLileTvSovPUWVDSGFI0bKzmXqEAAA7JErHo189NFHysrK0rBhw/Tpp5/u8nWlpaUqLS2VJM2cOVN+vz+mdblcri63MbSgRZFPt0reTKUWBBRauzrm9SWr7vQL4ou+SUz0S+KibxJTPPslLkFr5cqV+vDDD/Xxxx+rublZjY2Nuu+++3TVVVd1eF1JSYlKSkraH1dWxnYdlN/v73IbaSYoSVq5YbNGpflktm5RRUVFnx2hS2Td6RfEF32TmOiXxEXfJKZY9EsgEOj0elyC1gUXXKALLrhAkvTpp5/q5Zdf3ilkJbod99IaldNPCjZJDfVSeobNlQEAgETFPlpd9E3QCsny50cvbt1sY0UAACDRxWVEa0djxozRmDFj4t1sj3lcDvlSndG9tPoXRi9WbJYGFdtbGAAASFiMaHVDfrpLlQ0tkr9AkmQqN9lcEQAASGQErW7wp6VoS32LrLR0Kd0nVRC0AADArhG0uqFtd3hjjOQvkKlkjRYAANg1glY35KWnqClkVNsckZVXGF2jBQAAsAsErW7Ib73zsLK+RcorkLZukYmEba4KAAAkKoJWN/jTozdpbqlvXRAfDnHmIQAA2CWCVjfk77BpqeVv3eKBdVoAAGAXCFrdkJnqlNtpqaK+RcqLBi22eAAAALtC0OoGy7KUl56iLfUhKccvORxs8QAAAHaJoNVNea1bPFguVzRscechAADYhS4fwbN8+XLl5+crPz9fVVVVeuaZZ+RwOHTBBRcoOzs7ljUmlPx0l8q2NUUf5BXKcN4hAADYhS6PaD366KNyOKIvf+qppxQOh2VZlh566KGYFZeI8tJSVB0MKxhq20uLqUMAANC5Lo9obdu2TX6/X+FwWJ988okeeOABuVwuTZ8+PZb1JZy8tjsPG1oU8BdINdtlgk2yUj02VwYAABJNl0e0vF6vtm/frs8++0xFRUXyeKLBIhQKxay4RPTNFg+h9sOl2eIBAAB0pssjWieffLJuuOEGhUIhTZs2TZK0YsUKDRgwIFa1JaS8HffSyiuUkaJBa8BgW+sCAACJp8tB63/+5380adIkORwOFRZG95DKzc3VFVdcEbPiElFumksOKxq0NKx1L62KTbJsrgsAACSeLgctSQoEAu1/X758uRwOh0aPHt3rRSUyl8NSrtcVPYYnwy95vEwdAgCATnV5jdaMGTO0YsUKSdILL7yg3//+9/r973+vuXPnxqy4RJWfnqLK+hZZliX5C2S48xAAAHSiy0Frw4YN2m+//SRJ8+fP14wZM3TbbbfpjTfeiFlxicrftju8JPkLGdECAACd6nLQMsZIkjZtio7eFBUVye/3q76+PjaVJbD89BRtbWhROGJk5RVIlZvafx8AAIA2XV6jNXLkSD322GOqqqrSIYccIikaunw+X8yKS1QFGSkKG6myoUV5/gKpuVmq2S5l5dhdGgAASCBdHtG68sorlZaWpsGDB+vcc8+VJJWXl+vUU0+NWXGJKuBzS5LKa6NbPEhih3gAALCTLo9o+Xw+XXDBBR2uTZgwodcL6gsCma1Bq6ZZ4/2tWzxUbpY1fH87ywIAAAmmy0ErFApp7ty5WrhwoaqqqpSTk6Ojjz5aZ555plyubu0S0efleJzyuBwqr22WhuVHL1YyogUAADrqckL6y1/+ojVr1uiyyy5TXl6eKioqNGfOHDU0NLTvFJ8sLMtSwJei8ppmWSluKTtXquDOQwAA0FGXg9Z7772nWbNmtS9+DwQCGjp0qK6//vqkC1pSdPpw9dam6AN/oQwjWgAA4Fu6vb0DogI+t7bUt6glHGnd4oERLQAA0FGXg9bhhx+uO++8U0uWLNFXX32lJUuWaNasWTr88MNjWV/CGpDpVsRIm+paopuWVm2VaWmxuywAAJBAujx1eOGFF2rOnDl69NFHVVVVpdzcXH3nO99RKBSKZX0J65stHpo1wF8gGSNt3SIVDrC5MgAAkCi6HLRcLpfOO+88nXfeee3XmpubddFFF+nCCy+MSXGJrD1o1TTLyiuUkaJ3HhK0AABAqy5PHXbGsqzeqqPPyUh1KjPVGd3iIa9AUnQvLQAAgDY9ClrJLuBzq7ymWcrMkVLcbPEAAAA62OPU4fLly3f5XLKuz2oTyHTr4431shwOqV8+WzwAAIAO9hi0/vSnP+32eb/f32vF9DUDfG69+WW1GlsicucVct4hAADoYI9B6/77749HHX1SIDNFkrSxtllD/AUyqz+TMSap164BAIBvsEarB9ruPPy6plnKK5QaG6SGOpurAgAAiYKg1QP9d9hLy2q981BbNtpYEQAASCQErR5IdTnkT3NF7zwsLJIkmY0bbK4KAAAkCoJWDwUy3fq6tlnK6y85XdLGr+wuCQAAJAiCVg8N8Lmjm5Y6HFJ+f0a0AABAO4JWDwUy3apvjqg2GJb6D2RECwAAtCNo9VD7nYe1zbL6F0kVm2RaWmyuCgAAJAKCVg/teLi0+g+UTETaUm5zVQAAIBEQtHooPyNFTksqr22JjmhJEuu0AACACFo95nJYKshwRzctLSiSLEuGdVoAAEAErV4xIDNFG2ubZaWmSrl5jGgBAABJBK1eEWjd4iFijNR/ICNaAABAEkGrV/T3udUcNtraEJIVGCht/lomEra7LAAAYDOCVi8YkPnNmYcqLJJamqWtFTZXBQAA7OaKRyPNzc2aMWOGQqGQwuGwDjvsMJ177rnxaDouApnfbPEwrv9AGSm6Tiuv0Na6AACAveIStFJSUjRjxgx5PB6FQiHdfPPNGj9+vPbbb794NB9zuV6XUp1WdERr1EBJktn4laxxh9hcGQAAsFNcpg4ty5LH45EkhcNhhcNhWZYVj6bjwmFZCmS6VV7TLCs9Q8rMljaut7ssAABgs7iMaElSJBLRL3/5S23atEknnXSSRowYEa+m4yLgc6usqin6gDsPAQCA4hi0HA6HZs2apfr6et11111av369Bg0a1OE1paWlKi0tlSTNnDlTfr8/pjW5XK5ea6O4oE7vbahVdk6uGoYOV9PCN9SvX799auQuXnqzX9C76JvERL8kLvomMcWzX+IWtNqkp6drzJgxWrJkyU5Bq6SkRCUlJe2PKysrY1qL3+/vtTZyXGGFjbR83UYFsvNkGupUueYLWdm5vfL5yaQ3+wW9i75JTPRL4qJvElMs+iUQCHR6PS5rtGpqalRfXy8pegfi0qVLNWDAgHg0HTcDs6J3Hq7fHuTMQwAAIClOI1pVVVW6//77FYlEZIzR4YcfrokTJ8aj6bgZlJUqhyWVVQV1+KDWOw83fSVr/wNtrgwAANglLkFr8ODB+t3vfhePpmyT6nIo4HNr3fagNG6A5PEyogUAQJJjZ/heNCQnVWVVwegCeO48BAAg6RG0etHQbI+21Leovjksq7BIImgBAJDUCFq9aEhOqiRFpw/7D5Sqt8k01NtcFQAAsAtBqxe1Ba2yKu48BAAABK1e1c/rks/t0NrtTdERLUXvPAQAAMmJoNWLLMvS4ByP1lYFJX+B5HIxogUAQBIjaPWyodmpWrc9qIjlkAoGcOchAABJjKDVy4bkpCoYNtpc19J65yEjWgAAJCuCVi8bku2RJJW1rdOq3CLT0mxzVQAAwA4ErV42KNsth6XoOq3+RZKJSJu/trssAABgA4JWL3M7o0fxrN0elBUYJEkyX62zuSoAAGAHglYMDM1J1dqqJqmwSHKlSBu+tLskAABgA4JWDAzJ8WhLfUj1EUsaOFRm3Rq7SwIAADYgaMXAkOxvjuKxBhdL69fIRCI2VwUAAOKNoBUDQ1uP4llbFZQGFUuNDVLlJpurAgAA8UbQioFcr0u+VKfKqpqiI1oS04cAACQhglYMWJalIdmpWrs9KAUGRY/iWbfa7rIAAECcEbRiZEhO61E8Dpc0YIjMeu48BAAg2RC0YmRodqqaw0ab6lqi04fr1sgYY3dZAAAgjghaMTIkJ3oUz9qqpuiC+IY6qXKzzVUBAIB4ImjFyMCs6FE8ZVXB9gXxWs+CeAAAkglBK0bcToeKMqNH8WjAEMnplGFBPAAASYWgFUNDsj1aW9UkKyVFCgySWceCeAAAkglBK4aG5KSqoiGkumBY1uDh0vrVLIgHACCJELRiqH2H+O2tO8TX1UrbKmyuCgAAxAtBK4aG5UbvPFy1tfGbBfHsEA8AQNIgaMVQtsel/r4UraxslIqGSA4HR/EAAJBECFoxNtLv1YqKRinFLfUfKLOeOw8BAEgWBK0YG+X3antTWFvqW6IL4tkhHgCApEHQirFReV5J0ucVjdEF8bXVUtVWm6sCAADxQNCKsUFZqfK4HFpR0cgO8QAAJBmCVow5HZb283uiC+IHDpUsFsQDAJAsCFpxMMrv1drtQTU53FLhAI7iAQAgSRC04mD/PK8iRvpia2PrDvEcxQMAQDIgaMXBfv2iC+JXVDZKg4ul6m0y27fZXBUAAIg1glYcZKQ6NTDLrZUVjbIGsUM8AADJgqAVJyP9Xq2sbFRk4NDoDvFfrrC7JAAAEGMErTjZP8+r2uaINjY7pcHDZVYus7skAAAQYwStOBnp/2adljVqnLT2C5mmBpurAgAAsUTQipMBmW5luFs3Lh01TgqHpS8+t7ssAAAQQwStOHFYVvs6LRXvLzldMiuX2l0WAACIIYJWHI3ye7W+uln1lksqHimzgnVaAADsywhacdR2wPSqykZZIw+Q1n8pU19nc1UAACBWCFpxNKKfVw5rhwXxJiJ9sdzusgAAQIwQtOLIm+LQ4OxUraholIaOlFLcTB8CALAPI2jF2Si/V6sqmxRxuqTh+8usYEE8AAD7KoJWnI3K86oxFNGG6mB0+vDrdTK11XaXBQAAYoCgFWejWjcu/byidUG8JLFLPAAA+ySCVpwVZKQoP92ljzfWS0NGSB4v04cAAOyjCFpxZlmWJgQy9MmmBoXkkEaM4dxDAAD2Ua54NFJZWan7779f27dvl2VZKikp0amnnhqPphPShP7pev2L7fq8okFjRx0gs+xDme1bZWX3s7s0AADQi+IStJxOpy666CINGzZMjY2N+tWvfqVx48apqKgoHs0nnAMK0+RySIvL63XAyHEyksyKZbIOO9bu0gAAQC+Ky9RhTk6Ohg0bJknyer0aMGCAtm3bFo+mE1JailOj89K0uLxeGjhESsuQWKcFAMA+J+5rtLZs2aKysjINHz483k0nlIMC6VpXHdTWpog0cizrtAAA2AfFZeqwTVNTk2bPnq1p06YpLS1tp+dLS0tVWloqSZo5c6b8fn9M63G5XDFvY1dKRnv15McVWlVjqWTi4ar9+D3lhJvlLAjYUk8isbNfsHv0TWKiXxIXfZOY4tkvcQtaoVBIs2fP1lFHHaVDDz2009eUlJSopKSk/XFlZWVMa/L7/TFvY1d8xqhfmksLV23S4cXFkqSt/3lLjiNPsKWeRGJnv2D36JvERL8kLvomMcWiXwKBzgdK4jJ1aIzRgw8+qAEDBui0006LR5MJz7IsTeifHt3mobBIysqRPltid1kAAKAXxSVorVy5UgsXLtTy5ct1/fXX6/rrr9fixYvj0XRCmxjIUENLRKsqm2SNnSizfLFMKGR3WQAAoJfEZepw1KhReu655+LRVJ8yrjBNTkv6qLxOow+cJPNOqfTFp9L+B9pdGgAA6AXsDG+jdLdTo/K8WryxXho9XnKlyCz9wO6yAABALyFo2WxCIENlVUFVRVzSqHEyn7wvY4zdZQEAgF5A0LLZxEC6JGlxeZ2sAw+RKjZJm76yuSoAANAbCFo2G5KdqhyvS4vL62WNO0SSZD553+aqAABAbyBo2axtm4clm+oVyfZLA4cStAAA2EcQtBLAxEC66psjWlXZKOvASdKalTK1NXaXBQAAeoiglQAOLEyXw5I+LK+XNW6SZCIyyz60uywAANBDBK0EkJHq1NiCNL27vkZm0DApK0dmKdOHAAD0dQStBHHU4EyV17aorLoluij+049lQi12lwUAAHqAoJUgDhvok9OSFq2riQatpkZp1XK7ywIAAD1A0EoQmalOje+frkXramRGHSiluGU+YZd4AAD6MoJWAjlycKa21If0RZ1hl3gAAPYBBK0EcmhRhlwOS2+vrYlu87B1i1S+3u6yAADAXiJoJZB0t1MTA+latL5WkQMOlsQu8QAA9GUErQRz5OBMVTWGtKIlTRoyQubDRXaXBAAA9hJBK8FMKspQqtOK3n34neOlDWUy69fYXRYAANgLBK0E43E5dPCADL27vlaRg4+WXCkyi96wuywAALAXCFoJ6KghmaoOhrW8ziFrwuEy/31LpqXZ7rIAAEA3EbQS0MRAurwuh95eVyPriBKpoV5m8X/sLgsAAHQTQSsBuZ0OHTowQ//ZUKuWEQdI/fJl3im1uywAANBNBK0EddTgTNU3R/TJ5sboqNbnn8hUbra7LAAA0A0ErQR1YGG6MtwOvbW2WtZ3JkuWJfPufLvLAgAA3UDQSlApTkvHDcvSu+trtc2bLe0/Xuad+TKRsN2lAQCALiJoJbDT9stRxEjzVm2XdWSJtK1CWrHU7rIAAEAXEbQSWKHPrUlFGXp99XY1j50kpftkFrEoHgCAvoKgleBOH5Wr2mBYC79ulHXoMTIf/0emvtbusgAAQBcQtBLcmHyvhuak6uUV26QjSqRQSOa9t+wuCwAAdAFBK8FZlqXTR+VqfXWzlqYUSIOKZRa+LhOJ2F0aAADYA4JWH3DUYJ+yPE69vGKbrBNOl8rXS0s/sLssAACwBwStPiDF6dCpI3L0YXm9ykceJvkLFHnteRlj7C4NAADsBkGrjzh5RLZcDkuvrq6WdfJZUtkqtnoAACDBEbT6iGyvS0cPydT8NdWqP/hYKStXkVefs7ssAACwGwStPmTKyBwFw0al6+plnfg/0splMmtW2F0WAADYBYJWHzIs16OxBWl6aUWVmo84QcrwKfLa83aXBQAAdoGg1cdccIBf2xpDem1dk6zJU6SlH8hsKLO7LAAA0AmCVh8zpiBNEwPpmvPpVtUfcark8crM+7vdZQEAgE4QtPqgi8bnqa45ohfWBWUde6rMh4tkNn1td1kAAOBbCFp90NAcj44ekqmXVmxT1VHflVwpMq8zqgUAQKIhaPVRF4zzKxwxen5tSNZRJ8r8Z4HMxg12lwUAAHZA0Oqj+vvcOnF4tv61ers2HXuWlOpV5LlH7S4LAADsgKDVh513gF8uh6W/rgnKOu08aflimWUf2V0WAABoRdDqw3K8Lp0+KlcL19Wo7KASKT+gyHOPyoRCdpcGAABE0OrzzhidK5/boaeWVck652Jp01cyb82zuywAACCCVp+X7nbq3AP8WrKxXv/JGSWNHi/z0l9l6mrsLg0AgKRH0NoHfHe/HA3LSdUjH25WwxkXS40NMi/9n91lAQCQ9Aha+wCnw9KVh/ZXdTCspzd7ZB1zssxbr8t8vd7u0gAASGoErX3E8H4enT4qV/9cvV2fH3GW5Ilu92CMsbs0AACSFkFrH/L9cX7lp7v0wLJahab8QPrsYxbGAwBgI4LWPsTjcuiKQwr1VU2z5uYdKo2dKPO3P8usW213aQAAJKW4BK0HHnhAl156qa677rp4NJfUJg7I0NGDM/X3z7bp63P+n+TLVuTBO2Ua6uwuDQCApBOXoHXsscfqxhtvjEdTkHTJxHx5XJb+tLxO5rLrpapKRR6/j/VaAADEWVyC1ujRo5WRkRGPpiAp2+vSpRML9FlFo/5clSOdMVVa8p5M6Ut2lwYAQFJhjdY+6rhhWTpzdK7mfbFdLxYdJY0/VGbOEzJrVthdGgAAScNldwE7Ki0tVWlpqSRp5syZ8vv9MW3P5XLFvA07XVPST9WhlXpySYWGnnGNDtp4rcwjdyn3rsfkyM61u7xd2tf7pS+jbxIT/ZK46JvEFM9+sUycFu5s2bJFd955p2bPnt3l95SXl8ewIsnv96uysjKmbditORzRjPkbtGprk/6/MQ7t/9ANUmCwHD+/TVaqx+7yOpUM/dJX0TeJiX5JXPRNYopFvwQCgU6vM3W4j3M7HbrxmCIVZKTojpVGG3/4S2ndGkUeniUTDttdHgAA+7S4BK17771XN910k8rLy3XFFVfozTffjEezaOVLdermY4vktCz976ZcVZ/3Y2npBzL/9yB3IgIAEENxWaP1s5/9LB7NYDcKfW7ddGyRbipdr1tS9tMtJ58r7+vPSbl5sr57rt3lAQCwT2LqMIns5/fql0cN0NrtQd2ZdbRChx0v88JfFHmXEUYAAGKBoJVkJg7I0E8P66+lmxt034izFdn/QJmn/qDI84/LbN9qd3kAAOxTEmp7B8TH8cOytL0ppCc/rlD2d6br4sy/ybzxosybL8s6/HhZJ54hq3CA3WUCANDnEbSS1Bn756qqMaSXVlQp5/BpOut7P5D51wsy75TKLHpDOugwOY48URo1TlZKit3lAgDQJxG0kpRlWfrRhHxtbwrr6SUV0vg8nXXBdFlTzpeZ/4rMv19VZPF/JG+arAMOkTXhcGnshITdewsAgERE0EpiDsvSVYf1l4z09JIKbapt1hWTCuU640KZ086TVnwis/g/Mkv+K/P+W1KKW9Y5P5LjuO/aXToAAH0CQSvJpTgtXXNEfxX6UvTc8q3aUt+iXx41QOnuFOmAg2UdcLDMhT+RVn+myGt/l/nrIzIFAVmjD7K7dAAAEh53HUIOy9IPDszTTw8r1PLNDfrVv9ZpS11L+/OW0ylr5AFy/PhXUv8iRR6+S6Zik40VAwDQNzCihXYlxdnKT0/RzIVf6/p/rtX+eV41tkTUGDJqCkXUFIoodMBPFa6tUWTeRhlvjSKK3sV4yYR8WZZl91cAACChELTQwbjCdM08abDuf2+Tymta5Emx5HE5lO1JkdflkMtpydoWlOPTj+TI76+tg8fq5RVVGpSVqhOHZ9tdPoDdaA5H5LAsuRz8owiIF4IWdjIoK1V3njR4N6/or0jkc5kXH1AkcKlu7X+QHv5gs4pzPSrO5a7EPYkYIwejf4izjbXNuurVMjWHjbwuh3ypDmW4nfKlOnX+AX6Nzk+zu0Rgn8QaLewV69RzpPGHyfH3x3Str1yZHqfufPtr1QXDdpeW0LY2tOiKl77Ukx9vsbsUJJkXPt+miJHOP6CfSoZnaUx+mvqlpejLbU3RLV4AxARBC3vFcjjkuPhnUmGRMh6+TT9f9Zy21jXr3ne/VsQYu8tLSKGI0axF5dpc16K5n23TO+tq7C4JSWJ7Y0jz11Tr+GGZ+v64PF06sUA/+05ANx1bpHPG+vVZRaO+3NZkd5lJ4a9LK/TwB5tU1RiyuxTECUELe83ypsnx69myfvj/NLKhXFO/eFEflDdo7pwFMtv4F/K3PfXxFn1e0aifHd5fI/0e/eG9Tfq6ptnuspAEXl5ZpVDE6H/277fTc5OLs5TqtPTqqiobKksu//xiu55dtlWvrtquK15ao78urVBjS8S2euqCYb2xervWVhGyY4k1WugRy50q66gTZY48Qad9tkQr/rtez5gi9Z91tyaEK+QeWixr6H6yho6UhoyQlZpqd8m2eHd9jV5cUaXv7pet44ZlaWxBmq55rUy/e/tr/e6kwUp18W8exEZDS1jzVlXp8EE+Dch07/R8htupY4dmaUFZtaYelK/MVKcNVfYNoYjRi59v0wufb9PwXI9O2S9bEwMZcnbh5oIVFY16+MNNmhhI1yUTC/TMJxV6dtlW/fOL7fr+uDyVFGd16XN61uL/hgAAIABJREFUKhwx+mRTveZ/Wa3/bqhTS8Qow+3QbSWDNCQndmts64JhbagOakNNs76qDmpDdbM21kX/oZniiN6gkeK0lOKwlOlxqV+aS/28LvVLS1G/NJfy06P//fb6VmOMttS36LMtjfqsokFVjSF5U5xKS3H8/+3deXiU1dn48e95ZjKTfZ3sEELCIpvssigugLZWrdiqbS22VN7aSt+6VV7R9udutS4FF6xWKfjWLnbFV+uOgMq+yyJ7CGRfJntmf87vjycGYgIJSBbI/bmuXMkkM/OcmZMzc899zrkfIsMMIsIMosJsXDE4odMeW3uU1j13nqeoqKhT79/lclFRUdGpx+htGgMh5r61n4JGjaFNMn1usmuOkF1fxDneEoaMGoIx/SpUYvJx76Mn9cv+Si/LDlbjDZr4ghpf0MQb0tgUTOkXy4XZse0GSYW1fn7xziH6xjn49aX9CLNZLxSbCut5aEUB03Pj+PnE9K54OF9ZT+obcdSJ+uXfuypZsqWcp77ej4FJEW1eJ7/ax63/yeOHo5L51rDWWS8Beyo8vLCuhEPVPs5NjaSg1o/bEyQlys7XBiZwaW4cceGtcxcul4u9R0q4851DOG2Kp7+eTXRTMLunwsPizVamOzfRyT0X9iE5qnPOLesNmizd5eb9A9VUNgaJcRhc2D+OselRLFxXgqk1j13Wj/SY1sF4R4VMjS9kUuMNcajKx8EqL3lVPvKqvFQ0Hp0qddgUmbEOMmIc2AxFIKQJmiYBEwIhk2pviMrGAN5gy/AkzFCkRoeRFh1GWoyDWl+InWWNVDbdd5TDICUqDG/QpDFg4gmY+EOacLvB698Z1OK+OuO1LCMjo83fS6AlbxqnXb0/xLbiBvKqfByq9pLn9lDhsdLjGY3lXFqynqkZTuK+dgWqT/9Wt+/qftGmCTu3YK54GzwNGLN/AYku/rO3isWby7AbilinDYfNwGk3cNoUNb4QhbV+ohwGU3PiuHxgQpvZAl/QZO57+bg9QeZfnt3qRfSPW8v5x85KbpuUztScuK56yKdMxkzPdLx+CYRMfvzGQfrGOnh4etYJ7+OXHx6mrN7Pi9/M7ZLMyulmak1jwCTcbpzW8hWNAet8sO/srSYxws7N41OZ2DeGoKlZX1DH23ur2V7aiN1QTMuJ49phSaREHx3ncQmJ3PL6Fg66vTzxtX6tskZaa1YfruP5dSWE2RT3XJjJkOTTuwN0U2E9L24ooawhyNiMKKblxHFen2jCbNaHxCM1Pu754DARdsVjl/XDFdl+sHeoysube6rYXtqIN2DiCVpBzbEMBZmxDvonhNM/3klWvJM+sQ6So8La/R/TTf1Z6QlS0RCgrCFAcV2Akno/JU3fI+wGQ1MiGZYSybCUCLLina0yXoGQVQcy5kuZWgm0mkigdfao84XYUFjPu5+Xsac6hN0MMql8O5PCalBpffC50vElpOBzRmJzRlBRXU+DP0hjo5fGRh+BYIjRuSlcMiCRxIjTM+OtG+rRqz5Er3gbyksgNh4Cfhqc0Sycehdr3TA+M5rbJqW3GqRaa3aVe3hnbxWrD9cR0jA8NZK06DAivkhZ2w12lXvYUFDP/VP7Mjo9qlUbQqbmvo+OsLfCw5NtvAj3NGfLmPGHTPxB3ZxZONMdr18+2F/N8+tKeHBqX0a18f93rDWH63j8k0LuuTCTiX1jOqupJ8XUmoqGIIV1fqo8QWp9QWq9IWp91lfdMd/r/CFMDbFOG5cNiOfyQfHHDRiCpsYTaP3me6war7WB4M09VVR5glwxOIHvj3QRGdb6NkdqfPxnTxUfHKhBa820XCvgSo128Or2Gv71WTFzL8jggn6xxz3ekRofj64soLwhyM8mpJ2WD15VniCvbCrl0/w6+sQ6+NmEtOOW8dhf6eVXHx4mKdLOry/NajM7FzI1GwrreXNPFTtKG3HYFOMzo4l12nDarde88DBFtMNGv3gnWXHOTlsWobX+SkWyJdBqIoHW2elQlZf3Pi9nRV4tjbT9QucwA0QGvU1fHkLKRl5MJoaCMelRTMuNY3zm0U9kJ6PWG+Tvb66mpLiCnNojDIhR5I4bRcKESew7UMhTq0uosMdwY3IDM742vt3BXOUJ8sGBaj7Nr6PWF8ITsKrof2HmSBfXDXed8PZ3vJ2HN6iZPTaF6blxPbbKfleMmUBIN0+vngytNZuKGjAUjEqParNWWcjULM+r4U/bKvCFTO6/pC+DXW1Pp3WnBn+IxZvLKK4P8INRye22sa1+CZman/8nD6dN8dvLs9v9nwqZmpvfOEBGTPvZr6+i3h9ifUE9qw/Xsr3UQ2SYQUKEjfhwOwkRdmIcNioaAxTU+ims9bfKktgNiHHaiXXYiAm3EeOwEeu06oHFOA12lXlYX1CPUjCpbwxXDE4gJyGcPRXWGp5dZR72VHjwhzTZ8U5Gp0cxOiOKockR2A3FrjIP7+6rZvWRWoImDEuJYNboFAZ14P+kojHAP3dW8v5+K+AalR7FpqIGrhmSyKwxKe3evs4X4olPCvmstJFrhiRy46jkE2Z+tNbsrfSyraQBQymcNtWUeVfUeEO8vqMCX1Bz/fAkvjU0sd3Xy52ljTyw/Ah94xzMvSCTWl+IioYA5Y0ByhqCbCysp7Q+gCvSzhWDErh0QPwJg9WeTAKtJhJond18QZND1T7CQgGc5UU4S/JxFhwgqroMoqIhKQVcKaikVHRVBYX//Dsf9TuflX0mUumzFnCmRIXhtBs4jnmBOccVwZTsWOK/9IksZGre21/FnzcU0qBtpOpGSoxovhgASZF2arxB4p02fnH4TQbvXIH6xvWoq29AGScX0JnaSleHTDr0QlRa7+e5tSVsL21kTHoUP5uY1qH0fVfrzDETNDV/+ayCf++q5BuDE/jBqGQcHQykq71BXlhXwrqCegDSosO4fFA803KOvhFsLW5gyZYy8qp8DEoKp9YXotob4lcXZzIi9cTZnq60pbiB59YWU+UJEu2wUesLcUn/WG4clUzSl/4nqj1BVh2uo8gDA+MMxmVEN2fpvshQ3XV+BlOyj59JOdY/dlbyx63lPHdFf7Lij25cCZqaZQdqyK/xEW6zzhbhtFvjzWEzUFjTREqppu9gcMzPSlHtDbL2SB1bihsImuCKtDM2I5qQ1lR5glR7g1R7QtT4QiRF2ukT67C+4pxkxjhIirQTG24jwm60GzSW1vt5e281HxyopsFvogDd1MbseCdDUyKJc9rYVtrI7vJGgiY4bYr4CDul9QGiHAaX9I/jawPjyYo7+Q08xwZco/vEcc/5qR2ejg2amlc2lvLOvmqGJEcwJiOKnIRwchLDm7P5h6t9rDxUy6f5tZQcc17aLxueGskt56XSJ7bjj2FjYT2/XlnAl+Jbwu0GAxKdfGNwAhP7xJyR08vHkkCriQRavdPx+kUf2I258FFCwRDbb5jHKp1MrS+IL6TxBTX+kLUAsrQ+gKFgdHoUF/ePY0KfaPZXevn9xlIOVfsYVn2A/4qvpP8NN+IJmhx0+zjg9rLf7cVpU/xwdArRNhP9pxfRn34AQ0ejRp2H6psDfbJR4ac/A6LLijAb6nm3OpJX9zRgNxSzx6YwNaf7s1sN/hBrjtSxKr8Owx5Gn2iD7Hgn/ROc9Ilznpb1MGX1AZ5aVcSeCg/nuCLYXeGhX7yTOyentzuduu5IHQvXldAQMJk50oUrMoy391axq9yDw6a4MDuWKk+QTUUNpESF8YNRyVzQL4Yqb4j7lh2mtD7AvCmZjM2MPuFxTK3ZWdbI8oO1bCluID7cRnqMo+krjPQYBzFOW/O0cbjdOKk3o8ZAiCWby3lvfzV9Yh3cNimdPnEO/rnT2uVmN+C6YS6m5saxsbCeT/Jr2VHaiKkh0mGj0R/CUDA8JZLz+kSzPK+WBn+IF67K6XA7ar1Bbvr3AablxnHLeWkAbC6qZ9GmMgpqrTUxAdMkeIoVCZIj7UzOiuH8frEMSgrv9P9tX9Bk5aFayhsCDEmO4JzkiFZTf56AyY7SRraUNFBS5+f8rBgu6Nf+JpeOqPeFyExLpqbKfdK3fXdfFUs/d1NcdzSQSgi3EemwUVjrx1BwbmokU7Jjmdg3BodNNb8O+oIaE01mjOOUnuPd5R4OuL2kRIWRHGXHFRVGVFj7Ae6ZRAKtJhJo9U4n6hddWYb53MNQfAR1w08xLvp6q+scrvGx4mANKw7VUtkYxGlT+EKaZOXjhzv+xuShmRg3/LTdLJXWGr3s/9D/+RvU11m/VAqS0yEjC+Vwgs0Amx0MGzicqIFDYOgoVHj7i1m1txG94VP0qg/hwO7m35dEJPH80O+xKyaLAbZGRp/Th3PTohjsimj3xV9rTUhb2TtTW7t7TuWTZyBksrGogY8P1bKhwNoCnhYdRlykg4MVjQRM62XDbhzdPZQZ62j+2dqGbd2XoRRKgdNmbbX+stWHa3l+XQlaw5zz0piSHcvGwnqeXVtMo9/kh6OTuWJwQqupwAZ/iFc2lfHRwRpyEpzcPjmDfsdkYfKqvLy9t4qVebXYDcV1w5O4cnBCi+mTWm+Q+z86wuEaH3edn8mkrJZrk7TWFNUFWJFXw4q8GsoagkTYDcZkROENmhTV+SmtD2Ae51U03K6IdVpTYokRNhIirJ8jvtSPIa35z55qyhsCXD0kke+PdLXI5pXU+fnD5rLmjB1ARkwYF/SL5YJ+sYzOzWDtngLWFdSzrqCOIzXWtvmfjk/l8kEnt6392TXFfJpfy0PTsvjbjgo2FTWQHhPGj8akcF5mNEopgqZu2olrEghpNGBq6/kyAdO0fqe19XtTa5x2g6y4U3vjP5N91feZxkCIvCofB91eDri9VHtDjM+M5vysGOJP03rV3kgCrSYSaPVO7fWL9jRi/v5J2LEJomMhNQOVmgkp6ai0THClQXIqZngUO8sa+SS/Flf+Lq5a9gLhF0xDfb/9IKvF8bSGqgo4koc+fBB95CCUFEIwAKYJoSCEQuDzgN9vBV6DhqHOHY8aPhbCI8DnPfrVWI/evAa98VPw+yC9L+qC6aiUdHR1FdRUYda4ebcuihVGBgdiszCx6syc4wonM9ZJnT9ErTdIrc+aamnwm5hat/mGbzea1m407Zh02gwcdtU83RpmU3iDJg3+EPV+63tjwMTUEBdu44J+sVyUbWUgkpOTKS0rp7DWT16Vl0PVPo7U+CisDVBa72813fBlceE2MpuyQBmxDkrq/HxwoIaBSeHcdX4GacdsLa/2Bnl+bTEbChsYmRZJ/4Rw3J4g1Z4gbk+QisYA/pDm2mFJXD/cddx1XZ6AiaE4bpBa7w/x0PIC9lV6+O6gKHBGUFTrp7DOT3Gdn3q/dftRaVFckmNlSI+9r6CpKW8IUFznp8Fv7b7yBEwaA9bzWOMNUdXU5ipvkAZ/2+mgjJgwbp2UfsIdZ9tKGthT7mFsZjQ5Cc7moOXLY6ao1s9+t5fzs05+iueg28sd7xwCICrM4DsjXHxjUMIprZsT8j7TU0mg1UQCrd6pI/2iQyFrWi9/P7q0CMqKoPpL6fnIKHClQkQU7NmOmnIZauack15v1VE6GIQDn6M/24j+bAOUFBz/yuERqPFTUOdPh5zBbX7K16aJ/sN8Gjau5fPr7mBnwgC2lzZQ0RAkxmktAI4NtxHntBPlMLApKxgzDLA1ZZECIav2l++Y71/suPOHrMuBpjoz0Q7rJMNRDoMoh40hyRGMTItq8UZ9or4Jmpqy+gBFdVZ9IbAyGVpb62MaAybFdX6Kaq0ApsprnRfzmiGJfH9kcptv5Fpr3t1XzZIt5ZhaWxmh8KMZoov7x3VokXJ7PAGTR19fy3aVaD3OSDsZsQ4yYxz0jXMysW90q/VRp+qLLNCXRYSd3FTjsb7Ka5n2ea1s7DH/g7/fUAJK8d3hScS2sftMdJy8z/RMEmg1kUCrdzrVftHeRigrhvJSdEUpVDR9ryxDjRiL+vasTguy2mxPWTF692fW/InTiXJGgNMJzghrrZez/VIOOhjAfOZB2LcT49b7UUNHdUHLj+90jpnGQAhvUHeoXEfI1M2LrTuD3r+LwG/uoTQiCdeoUUTcdGunHKeznPKYqSzHfOhWGDgM4yf/gwo79WKVom3yPtMzdWWgJR9VxFlDhUdCVi5k5dITJjlUSjoq5atVfFf2MIxb7sF8Yh7m7x7DmPsYKivnNLWwe0WG2ehokqgzdzjpYBDztd9hS0wic8Q49Mfvoy+/BpXet9OO2VOYf33Zmr7eth7zuYcx5tzbKZs9hOjN5ARrQvRwKjIK49b7ISIK89mH0JVlJ7y+rq1Gb9uAufJddK2cKLg9+qO3oDAf47s3o66eCQ4n5ht/6u5mfWW6vvbEf9+2AbauRX3z+6gf3Q67t2MuuB/dWH/C2wkhTo5ktIQ4A6hEF8ZtD2D+5m7M+//bWnuWmIxKSIIEl7UAP38/+tA+OCYQ03/9vbUWbOqVqOyBLe5T+3yQtwd9aB8qOR0GDUfFdKzeUmfQwQB65Xvo1cswZsxEjRjb+cd0V6D/7y8wYhyMmoBSCnXp1ei3/orOP4Dql9vpbegM5toV6EW/Rc2YiXHF9a3+rn0+zL+8ZG3EuPSbKHsY2hmO+fJTmE//CuP2B1ExPf+UUGcz8+P3YN8u1I1zrB3O4owlgZYQZwiVmYXxi0fQqz5AuyugqsIKrOpqrCu4UlE5g2HqFajsQRAZhf7kffSqZeg1yyH3HNSkqVBZit67Ew7tt3ZMQnPRVjL7oQaPQA0eDueMREW2X8hTmyHwesDTaH15GyE2HpXS9nqFVrfXGjavwfzXq9Yau8hozOcfRn3vJxgXX37Sz9PJMP/2CpghjO/d3Lz+S116NfqjtzCXvobttvs79fidQVe70X95CRxO9NLXMMMjMKZd1fI6//krVJZhzP01ym7N36qxkzGcv8R84THMJ+/FuOMhK5AXXUoHg+jXX7FODQYQ8MPNc7t0fak4vSTQEuIMovrltsqy6IAf/H5UVOuCm+p7N6NnzESvXob+6C30ay+AzQbZA1GXXo0aNAyyB0FZEXrPdvSeHehPP7Cm0+x2GDYGNe4C1MjzUBFW2QHt98GeHegdm9A7NlnBUasDK9TkaagZM1Hxicd9PPrAbsy//8GqI5aRhXHrfTBwGObvn0T/6XeYFSWob/2wU95k9I5NsGm11cbktKNNj4xCXf5t9D9fRe/bhRo49LQfu7NorTFfewECAYxf/RZz6Wvov76M6QzHuOBS6zpFh9HvL0VNmooaNLzF7dXwsRi3P4D53MOYv/2VtSYwNr47HsoZRft96P99HjX2fNToiad+Pw31mC/9Bj7fhrrsGoiORf/rVXgjE3XNzNPYYtGVZNeh7AbpcaRfOoc2TSg+Aq40lPP4UxE6GIC8fVatr02rrBpi9jAYPgaHUvh3bLY+ZTscMPhcVL8BVimN8AgrAxYeid61Bb3MCtbU17+NumxG8/SHLi1Cb16N3rQa8vdDXALq6u9bgZnNqtqtQyH0X1+2PtWPnYxx0x2ndfpEB/zWFKxhw7j/WVRYy1X52ufD/OXNkJqBcdevm7NdOhSCLWvQOzajJrcOVLrLF2PGXLscvWg+6rqbMC6bgQ4EMBc+Aru2ov7rF6jxUzCf+iUUHMJ45HfHnR7U+3ZhLrgP0vpg/OLRDmU2ezPz9UXoD98Au92adh08ovlvHX0908UFmM8/Au4y1MyfYZw/zSqa/MeF6E/eR/3oNozJ0zrzYfQqUt6hiQRavZP0S8+hTRMO7kFv/BS9ZQ228EjMISOtQqyDh5+wHIAuK8b85xLYvAYSXahxU9C7tkDBIesK/QdZ2bILv9bmTjetNfqDN9D/WAz9B2Fc+R3ol4uKTWh1PUoK0Lu2ofd8Zk1fhkdY5TPCI6xyGtq0CsU2NkBjA1RVQlkRxp0Po4aMbLP95vL/oP/8EsbtD0LOYGvK9sM3rTVwNjuEgqiJF6Ou/REq7uSqr7dov2laAWdDHSQmW2vvTnLnn8vlonz/Xit4TO+D8T+PoYymoNXnw3zmfji4BzVpKvrTD1A3zsG4sPVZFVq0a/smK0jLGYxx24MnDM7bfYwlBeit61AXf+Os29Wo92zHfOqX1nN7aB9UuzHufhyV2Q/oQAHmulr0ltXof7xqBWpz7kENOJpF1cEg5jMPwL5dGHc+dFqDex0IYP7uMVRSCuq6H/WqtWASaDWRQKt3kn7puU6lb/TeHZivL4IjByF3CGrsZNSYSajE5I7dfvMazEW/tcoQgLX4v98AVN/+UF6C3r3taLFaVyrEJ1prxnzepu8eQEFktHWy8ogoiIpGDRnZau1Si+MGA5i/usWq/u9tWn82cCjGpTNgyEj0u/9Ev/cvCHOgvnkD6pIrmjNy7T4mrwd2bUF/tgH92caj6+y+EBltBV0JSajYOIiNt75i4q1pzuyBLaZTk5KSKHvwDti1FeO+Z6wzJBx7PE8j5m//HxzaZwVOd/+mQ9Ox5oZP0S8/CcPHWgGA/eSKtmqt0SvfRf99kXXWhIwsq4REasfW7/V02tOI+eCtYLNh3PcM1NdhPjYXDANj3hOoRFebY0a7y9Fb1qG3rIG9O60PAlm51nOclNL6OA31mI/PhbpajHuf7PD6x/aYf30ZvexN60Kf/hg/vfus6Zv2SKDVRAKt3kn6pec65cKYpgl+3ylnM7S3EQ4fRB/aD/kH0Pn7obQQomOtjNQ556KGjGyx1up0MNetRC9egBoz2VrT1n9Qy3aVFlm793ZusTYSDBpuBXOR1peKjEJ7GqGmCmrcUO1GV1daGaxgECKiUMPHwMjzUInJaHc5uCvAXW79XF0JtdVWIBYKHT1wU4ZQnXchZOUQvXMTtc881Dxl2OZz2FCHfuNP1g7UtD4dfw4+fg/9x4XW7tX/urM5U9YeXVeD+epzsG29df7P86ej//yStflg9p2oked1uA09lbnkWfTqj6wMVu45AOgjeZhPzIOkFIz/eZzkrH5UVFRYz/+GT9Frlx89t2l6X9ToSagxkyAr54QFeXVZMeZjd0FEFMZ1N8HI8R3uizbvb+s6zIWPoqZ/EzV0FOai+RAKYsy6FTX2/FO+3zOFBFpNJNDqnaRfeq6e1Dfa57WySZ28G0sHgyj78fcNaa1hy1rMN/8C7nIr89XWy2pEJMQlQFwiKivHCjRyh5zwvlsco7EeamvQ+fvRGz6BnZut4Cs1E1Vfi07LbDFleDqZ7/0L/Y8lMGQkasLFqHPHnbD8g96xCXPJs9BQh/r2D1FTr0IZBrqiFPN3j8PhA6grv4O66runrb3aNK31c5XlqIy+kJEFCa7OO5vAtvWYzz+CuvxajG/9oOXfdm3FfPZBGDCUuGu+T837/wefrbeC68x+qPMutLK6JxHwQtPmkZefsqavUzKs0hyTpp30tK52l2M+dLsVDM57AhUWZp0l4KXfQN5e1LSrUNfOOukM5plEAq0mEmj1TtIvPZf0Tfu0aVrBVmO9tR4sPMJa8H+a1ybp+lprU8H6TzBKC+EXj5z0G/fJMN9fiv7gDSvLppRVLuTc81DpfawiuuUl1imvykug6LA1TfjjX6D69G/Zbr8P/acX0auXWZnIvv2tE7QHgxBoOlH7OSOsKebI1jtp26IP7MZ8/RXI29vyD+ERkN7Xmk4OhaxjhILW8SKjUaMmWAFPdNv143QwCBWlVgbxmPVLuq4W84H/htgEjF8+1WZAYq5Zjv7DfOtCTBxqwkWoSZdA3xNnrtp9rKGQtVHlg6XW442OQV14Oer8aR06E4UOhTCf/iUczsP4f/NbTBXqYMDabfvh/0FmP4zrbkING33Kbe3JJNBqIoFW7yT90nNJ3/RMXdUvWmtrCnfbevS29XD4wNE/OsOtNXLJaah+A1rsNG3rfvTKd9H//l8rALKHQViY9T0YtII5e5gVCE28BIaNbjPzpyvL0f96Fb3+YytT+K0brY0aJQXoosNQdMT6Xl9rbWCw263yJvYwKytUVmxdHjLSmoodNAwKDqEP7EYf2G1N8Qb8VmDpSoWMLFRGFjr/AOzZjvGrp1sFki3at3UdsfHx1PbJ7VDm8mRorWH/55jvL4Vt66wsav9BVrZs/JTjbtAw3/gz+q2/ombfgTHxkuO223z9FSvIHD4G49qbUJlZp7X93U0CrSYSaPVO0i89l/RNz9Rd/aLdFdbaM1eqtV7uNEzTaa3h0H70mo/QGz6G+jqIjoXUDHA4weG0Ajil0FvWAlhB3de/fVJZQ601HDmIXv8JeuOnLc6ogM1urZnKHQKZWdaauabAjbIiCIVQ3/4hxte/3e5xuqJvtLvcWv+1fiUcPgjKsHYFZw+ElHRr8XxKOpQWYv72PtTEizFuuv3E9xkIoJe/hX7rb+D1oKZcZk33niVFbCXQaiKBVu8k/dJzSd/0TGdrv+hgAHZstsqL1FZbO0/9PmsHY8CHGjgMdc0PUEkd28F63ONoDXl7rdMu9e1vlRE5TukSHQxY5UFcqR0KLLu6b3TxEfT6j60gtLTQyhAeKzUT41e/7XBQqutr0W+9btW0C4Wsx91/EOQMtr5n5baqQ3cmkECriQRavZP0S88lfdMzSb/0XN3ZN9oMWbtYy4rRZUVQ7bYK7Z5CeQhdVmSVpMjbAwf3WoWMwSpcPHQ06tzxqJHjW9W5605aa2sq+MjBVsVeuzLQklPwCCGEEGchZdisaV1XKmroqK92XykZqK9d03xZV1fCwb3o3dvQ2zagt65DK2WtExs4DPxeqK9D19daa+R8PkhORWVkWTsvM7IgrQ8YNmuDQih4dLNCeIS1WaGNHcXa520ugYJSVr25pOTmDKQ2TTi0z9oosnmNtTkjzIEee75VxLgbSKAlhBBCiJOi4pNgjFUDTH/vJ1bmaNs69Nb16Pf/bRUGjo6F6BirzEaYw8qK7dkBwQDtTqUpw7ptdCzExFrFh93l1pq9tsQLNsiBAAAL5ElEQVTEWUFXbbWVbbPZrF2tX/+2tamim4IskEBLCCGEEF+BUgr69rfWt135XbTWx12/ps0QlJdC0WF0aSForN2gdru1CcFms8qj1FmZMF1fY2XE4hJROYOPnqYqwQVaNxX5tb50ZZmV3brmRmsqM6pj5UE6mwRaQgghhDhtTrRJQBk2awdpagano5Rs55SjPb06t6SyEEIIIUQvJoGWEEIIIUQnkUBLCCGEEKKTSKAlhBBCCNFJJNASQgghhOgkEmgJIYQQQnSSLivvsHXrVhYvXoxpmkybNo0ZM2Z01aGFEEIIIbpFl2S0TNNk0aJF3HvvvcyfP59Vq1ZRUFDQFYcWQgghhOg2XRJo7d+/n7S0NFJTU7Hb7UyePJkNGzZ0xaGFEEIIIbpNl0wdut1ukpKSmi8nJSWxb9++Vtf78MMP+fDDDwF4/PHHcblcndouu93e6ccQJ0/6peeSvumZpF96Lumbnqkr+6VHnYJn+vTpTJ8+vflyRUVFpx7P5XJ1+jHEyZN+6bmkb3om6ZeeS/qmZ+qMfsnIyGjz910ydZiYmEhlZWXz5crKShITE7vi0EIIIYQQ3aZLAq3c3FyKi4spKysjGAyyevVqxo0b1xWHFkIIIYToNl0ydWiz2bjpppt49NFHMU2TSy65hL59+3bFoYUQQgghuk2XrdEaM2YMY8aM6arDCSGEEEJ0O6kML4QQQgjRSSTQEkIIIYToJBJoCSGEEEJ0EqW11t3dCCGEEEKIs1GvzmjNmzevu5sg2iD90nNJ3/RM0i89l/RNz9SV/dKrAy0hhBBCiM4kgZYQQgghRCexPfDAAw90dyO6U05OTnc3QbRB+qXnkr7pmaRfei7pm56pq/pFFsMLIYQQQnQSmToUQgghhOgkXXYKnp5k69atLF68GNM0mTZtGjNmzOjuJvVaFRUVLFy4kOrqapRSTJ8+nW984xvU19czf/58ysvLSU5O5o477iA6Orq7m9vrmKbJvHnzSExMZN68eZSVlbFgwQLq6urIycnh5z//OXZ7r3wZ6VYNDQ28+OKLHDlyBKUUt9xyCxkZGTJmutlbb73FRx99hFKKvn37MmfOHKqrq2XMdIMXXniBzZs3ExcXx9NPPw1w3PcVrTWLFy9my5YtOJ1O5syZc1qnFXtdRss0TRYtWsS9997L/PnzWbVqFQUFBd3drF7LZrNx4403Mn/+fB599FHee+89CgoKWLp0KSNGjODZZ59lxIgRLF26tLub2iu9/fbbZGZmNl9+7bXXuOKKK3juueeIiorio48+6sbW9V6LFy9m1KhRLFiwgCeffJLMzEwZM93M7Xbzzjvv8Pjjj/P0009jmiarV6+WMdNNLr74Yu69994WvzveGNmyZQslJSU8++yz3HzzzbzyyiuntS29LtDav38/aWlppKamYrfbmTx5Mhs2bOjuZvVaCQkJzZ8cIiIiyMzMxO12s2HDBi666CIALrroIumjblBZWcnmzZuZNm0aAFprdu7cycSJEwHrhUz6pes1Njby+eefM3XqVADsdjtRUVEyZnoA0zTx+/2EQiH8fj/x8fEyZrrJ0KFDW2V0jzdGNm7cyIUXXohSikGDBtHQ0EBVVdVpa0uvy1+63W6SkpKaLyclJbFv375ubJH4QllZGXl5eQwYMICamhoSEhIAiI+Pp6ampptb1/ssWbKEmTNn4vF4AKirqyMyMhKbzQZAYmIibre7O5vYK5WVlREbG8sLL7xAfn4+OTk5zJo1S8ZMN0tMTOSqq67illtuweFwMHLkSHJycmTM9CDHGyNutxuXy9V8vaSkJNxud/N1v6pel9ESPZPX6+Xpp59m1qxZREZGtvibUgqlVDe1rHfatGkTcXFxsi29BwqFQuTl5XHZZZfxxBNP4HQ6W00TypjpevX19WzYsIGFCxfy0ksv4fV62bp1a3c3SxxHV46RXpfRSkxMpLKysvlyZWUliYmJ3dgiEQwGefrpp5kyZQoTJkwAIC4ujqqqKhISEqiqqiI2NrabW9m77Nmzh40bN7Jlyxb8fj8ej4clS5bQ2NhIKBTCZrPhdrtl7HSDpKQkkpKSGDhwIAATJ05k6dKlMma62fbt20lJSWl+3idMmMCePXtkzPQgxxsjiYmJVFRUNF/vdMcFvS6jlZubS3FxMWVlZQSDQVavXs24ceO6u1m9ltaaF198kczMTK688srm348bN46VK1cCsHLlSsaPH99dTeyVbrjhBl588UUWLlzI7bffzvDhw7n11lsZNmwYa9euBWDFihUydrpBfHw8SUlJFBUVAdYbfJ8+fWTMdDOXy8W+ffvw+XxorZv7RcZMz3G8MTJu3Dg+/vhjtNbs3buXyMjI0zZtCL20YOnmzZt59dVXMU2TSy65hG9961vd3aRea/fu3dx3331kZWU1p3G/973vMXDgQObPn09FRYVsVe9mO3fu5M0332TevHmUlpayYMEC6uvr6d+/Pz//+c8JCwvr7ib2OocOHeLFF18kGAySkpLCnDlz0FrLmOlmf/vb31i9ejU2m43s7Gx++tOf4na7Zcx0gwULFrBr1y7q6uqIi4vj+uuvZ/z48W2OEa01ixYtYtu2bTgcDubMmUNubu5pa0uvDLSEEEIIIbpCr5s6FEIIIYToKhJoCSGEEEJ0Egm0hBBCCCE6iQRaQgghhBCdRAItIYQQQohOIoGWEKJXu/766ykpKenuZgghzlK9rjK8EKLn+tnPfkZ1dTWGcfQz4MUXX8zs2bO7sVVte++996isrOSGG27g/vvv56abbqJfv37d3SwhRA8jgZYQoke5++67Offcc7u7Ge06ePAgY8aMwTRNCgsL6dOnT3c3SQjRA0mgJYQ4I6xYsYJly5aRnZ3Nxx9/TEJCArNnz2bEiBEAuN1uXn75ZXbv3k10dDRXX30106dPB8A0TZYuXcry5cupqakhPT2duXPn4nK5APjss8/49a9/TW1tLRdccAGzZ89u94SzBw8e5Nprr6WoqIjk5GRsNlvnPgFCiDOSBFpCiDPGvn37mDBhAosWLWL9+vU89dRTLFy4kOjoaJ555hn69u3LSy+9RFFREQ8//DBpaWkMHz6ct956i1WrVnHPPfeQnp5Ofn4+Tqez+X43b97MY489hsfj4e6772bcuHGMGjWq1fEDgQA//vGP0Vrj9XqZO3cuwWAQ0zSZNWsW3/zmN+WUXkKIFiTQEkL0KE8++WSL7NDMmTObM1NxcXFcccUVKKWYPHkyb775Jps3b2bo0KHs3r2befPm4XA4yM7OZtq0aaxcuZLhw4ezbNkyZs6cSUZGBgDZ2dktjjljxgyioqKIiopi2LBhHDp0qM1AKywsjCVLlrBs2TKOHDnCrFmzeOSRR/jud7/LgAEDOu9JEUKcsSTQEkL0KHPnzj3uGq3ExMQWU3rJycm43W6qqqqIjo4mIiKi+W8ul4sDBw4AUFlZSWpq6nGPGR8f3/yz0+nE6/W2eb0FCxawdetWfD4fYWFhLF++HK/Xy/79+0lPT+exxx47qccqhDj7SaAlhDhjuN1utNbNwVZFRQXjxo0jISGB+vp6PB5Pc7BVUVFBYmIiAElJSZSWlpKVlfWVjn/77bdjmiY333wzv//979m0aRNr1qzh1ltv/WoPTAhx1pI6WkKIM0ZNTQ3vvPMOwWCQNWvWUFhYyOjRo3G5XAwePJg///nP+P1+8vPzWb58OVOmTAFg2rRpvP766xQXF6O1Jj8/n7q6ulNqQ2FhIampqRiGQV5eHrm5uafzIQohzjKS0RJC9Ci/+c1vWtTROvfcc5k7dy4AAwcOpLi4mNmzZxMfH8+dd95JTEwMALfddhsvv/wyP/nJT4iOjua6665rnoK88sorCQQCPPLII9TV1ZGZmcldd911Su07ePAg/fv3b/756quv/ioPVwhxllNaa93djRBCiPZ8Ud7h4Ycf7u6mCCFEh8nUoRBCCCFEJ5FASwghhBCik8jUoRBCCCFEJ5GMlhBCCCFEJ5FASwghhBCik0igJYQQQgjRSSTQEkIIIYToJBJoCSGEEEJ0Egm0hBBCCCE6yf8H+iwMeyQ4incAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Early Stopping"
      ],
      "metadata": {
        "id": "6ndvTCMITxqn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " Early stopping is a technique that can avoid over-training and hence overfitting of the model. An over-trained model has a tendency to memorize all the training data points. With early stopping, a large arbitrary number of training epochs is specified. The model is stopped from training further when the model performance stops improving on the validation dataset."
      ],
      "metadata": {
        "id": "b2TmkIk4dzEP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(80, input_dim=60, activation='relu',kernel_regularizer=tf.keras.regularizers.l2(0.1)),\n",
        "    \n",
        "    keras.layers.Dense(30, activation='relu'),\n",
        "    \n",
        "    keras.layers.Dense(15, activation='relu'),\n",
        "    keras.layers.Dropout(0.1),\n",
        "    keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "H = model.fit(X_train, y_train, epochs=100, batch_size=8, validation_split=0.3,callbacks=tf.keras.callbacks.EarlyStopping(monitor='val_accuracy',verbose=1, patience=5)\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zGOnf7boTh1i",
        "outputId": "66192936-ca27-490b-a183-61e32b96d1b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "14/14 [==============================] - 2s 46ms/step - loss: 6.9748 - accuracy: 0.5413 - val_loss: 6.2898 - val_accuracy: 0.5957\n",
            "Epoch 2/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 5.7806 - accuracy: 0.5963 - val_loss: 5.2030 - val_accuracy: 0.5745\n",
            "Epoch 3/100\n",
            "14/14 [==============================] - 0s 8ms/step - loss: 4.7828 - accuracy: 0.5872 - val_loss: 4.3015 - val_accuracy: 0.6170\n",
            "Epoch 4/100\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 3.9594 - accuracy: 0.5688 - val_loss: 3.5644 - val_accuracy: 0.6170\n",
            "Epoch 5/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 3.2859 - accuracy: 0.6606 - val_loss: 2.9672 - val_accuracy: 0.6596\n",
            "Epoch 6/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 2.7376 - accuracy: 0.6147 - val_loss: 2.4843 - val_accuracy: 0.6596\n",
            "Epoch 7/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 2.3003 - accuracy: 0.6606 - val_loss: 2.0929 - val_accuracy: 0.6596\n",
            "Epoch 8/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 1.9592 - accuracy: 0.5688 - val_loss: 1.7809 - val_accuracy: 0.6809\n",
            "Epoch 9/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 1.6685 - accuracy: 0.6697 - val_loss: 1.5341 - val_accuracy: 0.6809\n",
            "Epoch 10/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 1.4420 - accuracy: 0.6606 - val_loss: 1.3311 - val_accuracy: 0.7234\n",
            "Epoch 11/100\n",
            "14/14 [==============================] - 0s 7ms/step - loss: 1.2668 - accuracy: 0.6422 - val_loss: 1.1732 - val_accuracy: 0.7021\n",
            "Epoch 12/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 1.1181 - accuracy: 0.7156 - val_loss: 1.0468 - val_accuracy: 0.7660\n",
            "Epoch 13/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 1.0137 - accuracy: 0.6789 - val_loss: 0.9797 - val_accuracy: 0.5745\n",
            "Epoch 14/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.9481 - accuracy: 0.5872 - val_loss: 0.8787 - val_accuracy: 0.7872\n",
            "Epoch 15/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.8514 - accuracy: 0.7523 - val_loss: 0.8059 - val_accuracy: 0.7872\n",
            "Epoch 16/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.7813 - accuracy: 0.7339 - val_loss: 0.7444 - val_accuracy: 0.7872\n",
            "Epoch 17/100\n",
            "14/14 [==============================] - 0s 4ms/step - loss: 0.7319 - accuracy: 0.7523 - val_loss: 0.7131 - val_accuracy: 0.7447\n",
            "Epoch 18/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.6758 - accuracy: 0.8165 - val_loss: 0.6691 - val_accuracy: 0.7660\n",
            "Epoch 19/100\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.6617 - accuracy: 0.7615 - val_loss: 0.6295 - val_accuracy: 0.7660\n",
            "Epoch 19: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "loss, acc=model.evaluate(X_test, y_test)\n",
        "print('Test Accuracy: ', acc*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSeDNKgqVngz",
        "outputId": "913c6b86-6425-42ce-b9e4-e45248df7b7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 5ms/step - loss: 0.6307 - accuracy: 0.7692\n",
            "Test Accuracy:  76.92307829856873\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "loss, acc = model.evaluate(X_train, y_train)\n",
        "print('Training Accuracy: ', acc*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmWTHvBfYdKe",
        "outputId": "64b14921-4c08-4da8-b176-fbc3d1cbd032"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 0s 3ms/step - loss: 0.6371 - accuracy: 0.7756\n",
            "Training Accuracy:  77.56410241127014\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "epochs = np.arange(0, len(H.history[\"loss\"]))\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.style.use(\"ggplot\")\n",
        "# (fig, axs) = plt.subplots(2, 1)\n",
        "# plot the *unshifted* training and validation loss\n",
        "plt.style.use(\"ggplot\")\n",
        "plt.plot(epochs, H.history[\"loss\"], label=\"train_loss\")\n",
        "plt.plot(epochs, H.history[\"val_loss\"], label=\"val_loss\")\n",
        "plt.title(\"Training Vs Validation Loss\")\n",
        "plt.xlabel(\"Epoch #\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "xKsDAiacYia3",
        "outputId": "f278e120-d796-4fe9-d935-6812a98fb968"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5e86375150>"
            ]
          },
          "metadata": {},
          "execution_count": 209
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ModelCheckpoint Callback\n",
        "\n",
        "A good application of checkpointing is to serialize your network to disk each time there is an improvement during training. We define an “improvement” to be either a decrease in loss or an increase in accuracy — we’ll set this parameter inside the actual Keras callback.\n",
        "\n",
        "\n",
        "ModelCheckpoint callback is used in conjunction with training using model.fit() to save a model or weights (in a checkpoint file) at some interval, so the model or weights can be loaded later to continue the training from the state saved.\n",
        "\n",
        "A few options this callback provides include:\n",
        "\n",
        "1. Whether to only keep the model that has achieved the \"best performance\" so far, or whether to save the model at the end of every epoch regardless of performance.\n",
        "2. Definition of 'best'; which quantity to monitor and whether it should be maximized or minimized.\n",
        "3. The frequency it should save at. Currently, the callback supports saving at the end of every epoch, or after a fixed number of training batches.\n",
        "4. Whether only weights are saved, or the whole model is saved.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "s_QPrLKKf6I2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "\n",
        "model = keras.Sequential([\n",
        "keras.layers.Dense(80, input_dim=60, activation='relu'),\n",
        "keras.layers.Dense(30, activation='relu'),\n",
        "keras.layers.Dense(15, activation='relu'),\n",
        "keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "checkpoint = ModelCheckpoint('SOMETHINGELSE', monitor=\"val_loss\", mode=\"min\",\n",
        "\tsave_best_only=True, verbose=1),\n",
        "earlystopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',verbose=1, patience=5)\n",
        "callbacks = [checkpoint,earlystopping]\n",
        "\n",
        "\n",
        "H = model.fit(X_train, y_train, epochs=100, batch_size=8, validation_split=0.3,callbacks=callbacks) #To discover the training epoch on which training was stopped, the “verbose” argument can be set to 1. Once stopped, the callback will print the epoch number., Add \"patience\" to wait for sometime before stopping after the best accuracy or val_loss has been found.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6KysNr_Yldb",
        "outputId": "f2eab836-5463-41a9-9232-95ef7136e29a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            " 1/14 [=>............................] - ETA: 7s - loss: 0.6916 - accuracy: 0.5000\n",
            "Epoch 1: val_loss improved from inf to 0.67181, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 2s 77ms/step - loss: 0.6927 - accuracy: 0.4771 - val_loss: 0.6718 - val_accuracy: 0.6809\n",
            "Epoch 2/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.6923 - accuracy: 0.5000\n",
            "Epoch 2: val_loss improved from 0.67181 to 0.64904, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 1s 63ms/step - loss: 0.6725 - accuracy: 0.6972 - val_loss: 0.6490 - val_accuracy: 0.8511\n",
            "Epoch 3/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.6475 - accuracy: 0.8750\n",
            "Epoch 3: val_loss improved from 0.64904 to 0.62424, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 1s 62ms/step - loss: 0.6592 - accuracy: 0.5872 - val_loss: 0.6242 - val_accuracy: 0.8723\n",
            "Epoch 4/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.6360 - accuracy: 0.8750\n",
            "Epoch 4: val_loss improved from 0.62424 to 0.60063, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 1s 106ms/step - loss: 0.6380 - accuracy: 0.7523 - val_loss: 0.6006 - val_accuracy: 0.8723\n",
            "Epoch 5/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.6607 - accuracy: 0.5000\n",
            "Epoch 5: val_loss improved from 0.60063 to 0.56512, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 1s 63ms/step - loss: 0.6088 - accuracy: 0.8073 - val_loss: 0.5651 - val_accuracy: 0.8723\n",
            "Epoch 6/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.5786 - accuracy: 0.8750\n",
            "Epoch 6: val_loss improved from 0.56512 to 0.54878, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 0.5939 - accuracy: 0.7248 - val_loss: 0.5488 - val_accuracy: 0.7872\n",
            "Epoch 7/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.6243 - accuracy: 0.6250\n",
            "Epoch 7: val_loss improved from 0.54878 to 0.50590, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 1s 61ms/step - loss: 0.5582 - accuracy: 0.7064 - val_loss: 0.5059 - val_accuracy: 0.8298\n",
            "Epoch 8/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.5423 - accuracy: 0.8750\n",
            "Epoch 8: val_loss improved from 0.50590 to 0.50098, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 1s 59ms/step - loss: 0.5625 - accuracy: 0.6697 - val_loss: 0.5010 - val_accuracy: 0.7872\n",
            "Epoch 9/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.5310 - accuracy: 0.7500\n",
            "Epoch 9: val_loss improved from 0.50098 to 0.45174, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 1s 63ms/step - loss: 0.5213 - accuracy: 0.8073 - val_loss: 0.4517 - val_accuracy: 0.8511\n",
            "Epoch 10/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.4473 - accuracy: 1.0000\n",
            "Epoch 10: val_loss improved from 0.45174 to 0.42931, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 1s 63ms/step - loss: 0.4697 - accuracy: 0.8532 - val_loss: 0.4293 - val_accuracy: 0.8298\n",
            "Epoch 11/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.4057 - accuracy: 0.8750\n",
            "Epoch 11: val_loss improved from 0.42931 to 0.40078, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 1s 63ms/step - loss: 0.4350 - accuracy: 0.8624 - val_loss: 0.4008 - val_accuracy: 0.8723\n",
            "Epoch 12/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.3505 - accuracy: 1.0000\n",
            "Epoch 12: val_loss improved from 0.40078 to 0.39537, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 1s 62ms/step - loss: 0.4137 - accuracy: 0.8807 - val_loss: 0.3954 - val_accuracy: 0.8298\n",
            "Epoch 13/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.5394 - accuracy: 0.7500\n",
            "Epoch 13: val_loss improved from 0.39537 to 0.37888, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 1s 62ms/step - loss: 0.3828 - accuracy: 0.8807 - val_loss: 0.3789 - val_accuracy: 0.8723\n",
            "Epoch 14/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.3250 - accuracy: 0.8750\n",
            "Epoch 14: val_loss improved from 0.37888 to 0.37033, saving model to SOMETHINGELSE\n",
            "INFO:tensorflow:Assets written to: SOMETHINGELSE/assets\n",
            "14/14 [==============================] - 1s 63ms/step - loss: 0.3655 - accuracy: 0.8716 - val_loss: 0.3703 - val_accuracy: 0.8511\n",
            "Epoch 15/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.5153 - accuracy: 0.8750\n",
            "Epoch 15: val_loss did not improve from 0.37033\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.3613 - accuracy: 0.8349 - val_loss: 0.3863 - val_accuracy: 0.8298\n",
            "Epoch 16/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.4189 - accuracy: 0.8750\n",
            "Epoch 16: val_loss did not improve from 0.37033\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.3158 - accuracy: 0.9358 - val_loss: 0.3829 - val_accuracy: 0.8298\n",
            "Epoch 17/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.2713 - accuracy: 0.8750\n",
            "Epoch 17: val_loss did not improve from 0.37033\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.3084 - accuracy: 0.8991 - val_loss: 0.3871 - val_accuracy: 0.8298\n",
            "Epoch 18/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.2297 - accuracy: 1.0000\n",
            "Epoch 18: val_loss did not improve from 0.37033\n",
            "14/14 [==============================] - 0s 5ms/step - loss: 0.2815 - accuracy: 0.9358 - val_loss: 0.3735 - val_accuracy: 0.8723\n",
            "Epoch 19/100\n",
            " 1/14 [=>............................] - ETA: 0s - loss: 0.1493 - accuracy: 1.0000\n",
            "Epoch 19: val_loss did not improve from 0.37033\n",
            "14/14 [==============================] - 0s 6ms/step - loss: 0.2834 - accuracy: 0.9083 - val_loss: 0.3861 - val_accuracy: 0.8511\n",
            "Epoch 19: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "loss, acc=model.evaluate(X_test, y_test)\n",
        "print('Test Accuracy: ', acc*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YEzcmMH8nxMU",
        "outputId": "93e9fa27-f40e-46e9-fe10-15b86ce47670"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 6ms/step - loss: 0.4752 - accuracy: 0.7692\n",
            "Test Accuracy:  76.92307829856873\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load best weight "
      ],
      "metadata": {
        "id": "SKuTOQUvn2QR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "keras.layers.Dense(80, input_dim=60, activation='relu'),\n",
        "keras.layers.Dense(30, activation='relu'),\n",
        "keras.layers.Dense(15, activation='relu'),\n",
        "keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "model.load_weights(\"/content/bestweights\")\n",
        "print(model.evaluate(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltNGj0hTjk2n",
        "outputId": "b553f008-0a32-4e5f-b829-33427a516b22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 7ms/step - loss: 0.4688 - accuracy: 0.7115\n",
            "[0.46880486607551575, 0.7115384340286255]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VySbwps1n_kd"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}