pass_fail.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "pass-fail.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMpkgMVY7UoUjfSc1YWvHGe",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sonichandni/pass-fail-prediction-model-TF2/blob/master/pass_fail.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KlYf64HNJBU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import feature_column\n",
        "from tensorflow.keras import layers\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpKD4-iXbXvy",
        "colab_type": "code",
        "outputId": "fbb93616-ff78-4dd2-926f-dbe5c92707a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "!wget https://raw.githubusercontent.com/sonichandni/pass-fail-prediction-model-TF2/master/StudentsPerformance.csv"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-02 07:38:36--  https://raw.githubusercontent.com/sonichandni/pass-fail-prediction-model-TF2/master/StudentsPerformance.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5018 (4.9K) [text/plain]\n",
            "Saving to: ‘StudentsPerformance.csv.1’\n",
            "\n",
            "\r          StudentsP   0%[                    ]       0  --.-KB/s               \rStudentsPerformance 100%[===================>]   4.90K  --.-KB/s    in 0s      \n",
            "\n",
            "2020-03-02 07:38:36 (87.5 MB/s) - ‘StudentsPerformance.csv.1’ saved [5018/5018]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3hUYLmRN1b3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"StudentsPerformance.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NrFDSByXOKHU",
        "colab_type": "code",
        "outputId": "c32ce135-2cd3-40ec-839b-d1d336db72f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>score</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>72</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>69</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>90</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>76</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   score  target\n",
              "0     72       1\n",
              "1     69       1\n",
              "2     90       1\n",
              "3     47       1\n",
              "4     76       1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02nRjYUBo9Hu",
        "colab_type": "code",
        "outputId": "6efd93d5-06e1-4c40-b999-05d7315d395c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>score</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.00000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>66.08900</td>\n",
              "      <td>0.960000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>15.16308</td>\n",
              "      <td>0.196057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>57.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>66.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>77.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>100.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            score       target\n",
              "count  1000.00000  1000.000000\n",
              "mean     66.08900     0.960000\n",
              "std      15.16308     0.196057\n",
              "min       0.00000     0.000000\n",
              "25%      57.00000     1.000000\n",
              "50%      66.00000     1.000000\n",
              "75%      77.00000     1.000000\n",
              "max     100.00000     1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9iuAsW_OOk9c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df.drop(['lunch', 'test preparation course', 'reading score','writing score'])\n",
        "# del df['Unnamed: 0']\n",
        "# df.columns\n",
        "# df['target'] = np.where(df['math score']>=40, 1, 0)\n",
        "# df.columns = ['score', 'target']\n",
        "# df.to_csv('StudentsPerformance.csv', index=False)\n",
        "# import matplotlib.pyplot as plt\n",
        "\n",
        "# df.plot(kind='hist', title='Normally distributed random values') # hist computes distribution\n",
        "# plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wMn4OOkkQr1Z",
        "colab_type": "code",
        "outputId": "d7f483bc-26a0-45e9-ec37-7d87defdb7ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "train, test = train_test_split(df, test_size=0.2)\n",
        "train, val = train_test_split(train, test_size=0.2)\n",
        "print(len(train), 'train examples')\n",
        "print(len(val), 'validation examples')\n",
        "print(len(test), 'test examples')"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "640 train examples\n",
            "160 validation examples\n",
            "200 test examples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xl9b6Zw7REYy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# A utility method to create a tf.data dataset from a Pandas Dataframe\n",
        "def df_to_dataset(df, shuffle=True, batch_size=32):\n",
        "  df = df.copy()\n",
        "  labels = df.pop('target')\n",
        "  ds = tf.data.Dataset.from_tensor_slices((dict(df), labels))\n",
        "  if shuffle:\n",
        "    ds = ds.shuffle(buffer_size=len(df))\n",
        "  ds = ds.batch(batch_size)\n",
        "  return ds"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XgelrQMMRSZl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 32\n",
        "train_ds = df_to_dataset(train, batch_size=batch_size)\n",
        "val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)\n",
        "test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PhFStNKi9H_",
        "colab_type": "code",
        "outputId": "0ff1a8b7-05ae-4e6b-8ec2-bb3c2bb8bd12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "for feature_batch, label_batch in train_ds.take(1):\n",
        "  print('Every feature:', list(feature_batch.keys()))\n",
        "  print('A batch of marks:', feature_batch['score'])\n",
        "  print('A batch of targets:', label_batch )"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Every feature: ['score']\n",
            "A batch of marks: tf.Tensor(\n",
            "[93 70 84 36 98 57 45 54 38 65 81 57 62 80 54 86 71 78 76 61 83 69 85 53\n",
            " 97 59 69 74 73 62 65 68], shape=(32,), dtype=int32)\n",
            "A batch of targets: tf.Tensor([1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1], shape=(32,), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZGemKvHjPKQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "example_batch = next(iter(train_ds))[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RStsxa9njVUE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# A utility method to create a feature column\n",
        "# and to transform a batch of data\n",
        "def demo(feature_column):\n",
        "  feature_layer = layers.DenseFeatures(feature_column)\n",
        "  print(feature_layer(example_batch).numpy())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RH1AjazOjYiZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# score = feature_column.numeric_column(\"score\")\n",
        "# demo(score)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgLbZik7jrJw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# score_buckets = feature_column.bucketized_column(score, boundaries=[40,100])\n",
        "# demo(score_buckets)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XvhlvHB7lT1T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature_columns = []\n",
        "for header in ['score']:\n",
        "  feature_columns.append(feature_column.numeric_column(header))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7i2_cmjvlGrz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature_layer = tf.keras.layers.DenseFeatures(feature_columns)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PTo--8JkbdT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "  feature_layer,\n",
        "  layers.Dense(128, activation='relu'),\n",
        "  layers.Dense(128, activation='relu'),\n",
        "  layers.Dense(1, activation='linear')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_ds,\n",
        "          validation_data=val_ds,\n",
        "          epochs=100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3sKCpya2tFd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_metrics(history):\n",
        "  metrics =  ['loss']\n",
        "  for n, metric in enumerate(metrics):\n",
        "    name = metric.replace(\"_\",\" \").capitalize()\n",
        "    plt.subplot(2,2,n+1)\n",
        "    plt.plot(history.epoch,  history.history[metric], color=colors[0], label='Train')\n",
        "    plt.plot(history.epoch, history.history['val_'+metric],\n",
        "             color=colors[0], linestyle=\"--\", label='Val')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel(name)\n",
        "    if metric == 'loss':\n",
        "      plt.ylim([0, plt.ylim()[1]])\n",
        "    elif metric == 'auc':\n",
        "      plt.ylim([0.8,1])\n",
        "    else:\n",
        "      plt.ylim([0,1])\n",
        "\n",
        "    plt.legend()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0dVyDvm2fM7",
        "colab_type": "code",
        "outputId": "68553851-6abc-4792-fa0d-566a9ec50c34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "import matplotlib as mpl\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "mpl.rcParams['figure.figsize'] = (12, 10)\n",
        "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
        "plot_metrics(history)\n"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vHs+t5xSE6qNMGZrCfzcdxO/XoRKs18/N5/m1pdz96nbmFmTw0vpyXtpQzjfO\nGcnVhUNJsJlJTbBhNZvw+zUJNjNbyhu4qnBo6HM2ldYzOTe5x6zSJpeX/Iy+yfq0mE3kpsbz1nfP\nPuprfX6Ny+MjwS7/1IVTD3Gh9BGXThnCsPQEtt29iK/Nj2x+/M9V+3ng3T3UNLeHwut8fn3cvuFw\nlFJkJtpDx1NzU2hyedkXVuDKajbx80smsL+mlTv/vZmfL9vG/NGZ/OCCMQzPSCAryRFKlDGZAt2F\nNpc1hK7/eE8Vlz38Ce/1Ihyxud3LmpI6Jv5sOTsPN0adU1bXGtHsoivK6lp56L09oezOo2HpyhLO\n/u370gxZOCURAe8HOlqnqfE29te2cM7vPuCh9wJNG17dfJCFf/iIj/dURcx9c+shLvrTx1Q29lwX\nPMh/NpTz3ec30uY+4isOum06Jr/MG5XBRZOy+c/Gg2Q4bfzxmqldNkmYMjSF7YcaQ40iXt0U8J+v\nDou66YpGl4eUOCvN7d4ue37O+/X7XPTAxz3eq6S6ld+9tZt7XtvBvUcZGbPhQD3VzW6a+7ioliDE\nAiLgJ4BByQ5cHj/T8lJDRaiS46xorbl56VrWGb7qospmvvP8RrYfaozwS/fEm1sPs3pfLQ7rkb/O\ngiwnV0zLYVBi54SjH39+PGePzuQv180grZv0/kk5ybi9fnZXNOHx+Vm+/TAAG/Z3nxGptWZUlpNx\nQ5IAQv70YyUYhbLjUCPvbK/oYXYk+4w2db2NfxeEgUS/CrhSapFSapdSqkgpdWeU83al1PPG+c+U\nUvnG+BeVUhvDXn6l1FTj3AfGPYPnsjreN9a4+azh/Pe2eTx9w8xQPPY5Y7J4/mtzyE5ycNPTayiq\nbCI/PZ6vnz2ScYOT+Pe6sl65WHx+zcriGuYWpEdY/maT4g/XTOXMgoxO1wxJiePpG2f1uLk6OTcZ\ngM1lDawqrqG+1UN+ejyby+sj2rd1RCnFS7fO5ZvnFAB0WVJ2cm4yC8b0XFwrGIWSEm87qjBCrTXF\nRhRNo0sEXDj16DcBV0qZgYeBC4HxwBKl1PgO024C6rTWBQQqHf4aQGv9T631VK31VOBLwD6t9caw\n674YPK+1PnH54cdIosPKpCgbfxlOO0tvPAOLycRv3twFwHfOH811s/PYU9nM9kM9+4e3HWygoc3D\n3ChCDXC4wYW3G7Htjry0eJLjrGwua+D1LYdJsJn55oICXB5/qGFFdyTYA9mYXVngjW0ekuKsUc+F\nExTt1HjrUSXyVDW102K4lRrbxIXSFe/uqIhoGCIMHPrTAp8FFBn9M93Ac8BlHeZcBjxtvH8ROE91\nDm9YYlx7SpKXHs9TN8ykzYxM1dAAACAASURBVOPDbQjtxZOH8OLX5zB+cFKP139SFFn7O5zl2w4z\n+//eZUcvxDYaSikm5yaz4UAdy7cd5txxg0Kfs6G06//hS6pbuPBPH7P9YCNXzcgNtX7ryIScZF7Z\neJDiqmZufGoNf3xnd9R5kRZ47x9Ge6uObOA2nIBM2IHKTU+v5Yo/f3qylyEcA/0p4DlAadhxmTEW\ndY7W2gs0AB2V6BqONJII8qThPvlJFMEHQCl1i1JqrVJqbVVVVbQpMcPEnGT+ftMZoSqEyXFWCvPT\negzVA3DazSwcPyjURDmc4ANg43FU8ZuUk8zOw03Utri5aGI2OSlxZCXaI2LMO1LXGugI5LCa+e1V\nU5g1PC3qvGuM8MTq5nZW7q0J7QV0mjczj/U/WUhBlpPUeGuvo3eC/m8QF0p3DE2LY8KQno0FIfaI\n6U1MpdQZQKvWemvY8Be11pOAs4zXl6Jdq7V+TGtdqLUuzMwceN3fGto8/OQ/WztFqXTkS3Py+euX\nC6Oey02NIz3BxsYDxy7gQT94nNXMOWOyUEoxLS+FDaVd3zMYKpnosKC1jpqJ6fX5qTTa05XXt9Hm\n8VHRReSNzWIiLcHGNxcU8Old5/XqwQawr7qZYIBNo1jgXTI0NR6HUXxMGFj0p4CXA0PDjnONsahz\nlFIWIJnIVm3X0sH61lqXG/9tAp4h4Ko55Yi3mXl9yyGe+exAl3O2ljd0W+1PKcWZBRm8u7PimGuI\nTDK6+iwYm0mcLfA/+fS8VPbXtFLdHL0/aLNRC9zpsDD9l29zz2udQ/+qmtv5/r82AbC/OhDffbgh\nuoC/t7OC37+166jXXlzVQkGWE6VEwLvj025+/QixTX8K+BpglFJquFLKRkCMl3WYswz4ivF+MfCe\nNn4fK6VMwNWE+b+VUhalVIbx3gpcDGzlFMRqNrF4Ri5vbD3Mj/+zpZMA1zS384W/ruLH/9nS7X2u\nKRxKfauH5dsOh8a8Pj8vrC3l4z1VNLo8aK0prW2NWrt7SLKD288bxTcXFITGphmVDruy7IPNHJx2\nCzaLKWoUSvimYnl9oNlyo8sbEcseZMWeGp78pISPdldx/ZOrQ12PemJfdQsjM50k2i00umQTUzj1\n6Lf8Yq21Vyl1G7AcMANPaK23KaXuBtZqrZcBjwN/V0oVAbUERD7IfKBUa10cNmYHlhvibQbeAf7a\nX9/hZPO9C8YA8OhHxazeV8sT188MNSX+3Vu7aXX7uPmsEd3dgjNHpnP/1VM4b9yg0FhJTQs/+c9W\n2r1+lAr43OtbPfzy8ol8afawiOuVUtyxcHTE2KScZCwmxYbSOs4fP4iOpCbYmD0ijaQ4Kwl2S8il\nEk5wU9FuMaHCEokqGl3kZyREzHV5fTisJqqa2vlgVxVNLk+38esQKNJ1oLaVCydls6W8QSzwLghG\nKHX8OxYGBv1aIEJr/Trweoexn4a9dwFXdXHtB8DsDmMtwIw+X2iMYrOYuOuicZxZkMF9b+wkxSiS\ntaWsgefWHODGucMZ1UOhKZNJceX03IixgqxEPv7hAnYdbmLjgXrK6tqYmJPE/FEZlNa2kppgw9lN\n7ZA4m5lxg5NY30VCz8Lxg1hoCHuCLVBStiNBQX3ha3Mifh0cjibgHh92iznkwulNKGFpbStev2Z4\nhpMkh1WiULqg3XDBhSeBCQMHqfAzADh7dCZnFWRgMik8Pj+XPLSC1Hgr3z6/+0714SxdWYLJ6I05\nY1gqWYkOshIdnDXqyAbvxtJ6Ln/4Ex65bgaLJmZ3e7/peSn8a10ZHp+/20bD8TZz1FT6YFRIcpyV\ng/VtmBT4NVE3Ml0eH3E2c0hkehNKGIxAGZGZQHKcVaJQuiDomrv39Z3cMn/kSV6NcLTIY3eAEKxX\n0tDm4ZIpQ/jdVVNIcvScBBPk/Z2V/P6tXVz3+Gc8+N6eqHNGDwps+O3oRQLROWOzaHX7+Nfask7n\n7nltO4v/EogrvmTKEC6a1LkK4/S8VO67chI/XbaN93dVhUrWRhdwPw6rCYfFbBz3bIEXGzHgIzIS\nSIqzSCJPF7SHbYL3Rfs74cQiAj7AyHDaeXDJtAifdm+4ZmYeda0e4q1mrj8zP+qceJuF4ekJXVYP\nDOec0ZkUDkvlj+/s7rTxeLDeRW2rG4/Pz3Wzh/GVKJ+Xn5HAtbPycBibnKMHJRJvM3O4oXNky+Nf\nKeSlb8wlKc7KiMyEUMncaARjxIurW0hLsJESbxMXSjdkJzn47vkB/3e0DWQhthEBP004b1wW0/JS\n+NHnx5HutHc5b9zgpF5lbiqluPPCsVQ2tfPEJ/sizjW1e7GYFIW/eoenPtkXtZDUvuoWtpY3kBJv\nxePTDE5xkJ3kiGqBK6WwWUxMzEnmve+dw8z8I4lBm8vqeX9nJVVN7dS2uPnCXz/jmc8O8O6OCrKT\nAslN4kLpGpPpSBli6Tc68BAf+GmC1Wzi5Vvn9jhv3OBEXttyiCaXh8QeXDSF+WmcPy6LRz7cyxdm\n5YUaWdS1uCmpbsXt8/Pb5bv47Vu72PaLRRHX/uWDIj7aXc3CCYFfEoOTHAzqQsD/9M4e6tvcfLyn\nGotJ4bAGsk+/uaCAH728ha3lgV8MNkvAHpmWl0JlU3uocFhSnJVWt69Hf/3pSGltK38wShh0VXRM\niF3kX7MQwaKJg/nzF6dHFbpWt5dvPbuBVzcfDI394HNjaW738stXt7OvuoU2t4/dFU34/H6+e/5o\nWtw+Wts7NzZubPOSFGcJ/QNMS7AxKMnO4SgC/srGct7aVsGh+jYON7rYX9PC31fuB+DFr5/Jc7fM\n5sefH8fiGbm88LU5XGw0dE6JDzyAkhwW4zPFCu9IWV0bVU3txFnN8nAbgIgFLkRQkOUMWa4dWVVc\nw383HSTJYeHiyQGRHJOdyJdnD+Pplft5aUM5cVYz7V4/1xQO5bZzC3j8k2Ia27y4PP5QGCAENmOT\nHFZSDZEdlORgULKDysZ2tNYR6fJtHh8NbR7mjEjn3Z2VzB+VwcdF1bi9fhxWM7NHpDN7xJESOiv2\nVAOQaA/cO9n4jEaXt1v30elIuzfgNvnnzWd0WXRMiF3kkSt0YmNpPauKazqNB1Pidx6O9JH//NIJ\nvHPH2dx7xSQWTczm1/8ziV8vnozZpJgzPCCs6w5EdvFpdHlIjrOSFBdwuxRkOclOcuD2+anr4DNv\ndftodfsozA9kgCbYLWgNOw818tWn17Byb+RaKxoDmZ3BB0YwWkc2MjsTDMm0W0QKBiLytyZ04t7X\nd/CbN3dGjNW3ukPlWXcdbopwiSilKMhy8oUz8vjDNVO5ZmZe6FwwzvyfqyJrujS6ArXADzW0YTMr\nkhwWBhmbjh1rogSjI4JWdrDw0o7Djbyzo7JTan1JTaC2SjAZKVhzXFwonQla4J9/YAXv7ji6bkfC\nyUcEXOjE+MFJ7DzcFNEIeINR9+TSKUNobvdSVtcW9doml4eJP1vOP1YFfNQzh6cxOTeZd3ZUsLvi\niOV+7xWTuP7MfHZXNOP2ad7cVhES8PCNTK01Go3FaLJst5hC1mKJkawT9HUH2VvVzIiMBL7/uUAp\nguSggEskSifCY7/lz2fgIQIudGJsdiKtbh8Hao90gV+3vw6zSbFkVh7jByd16Y5obvfS3O4NxWqP\nyU7kT9dOI95m4fMPfMx9b+yk1e3lrFGZTBmaQm1LIO67vtVNdnJnAVdKMTY7icL8VKxmE9PyUhhu\npNqXGg+R5A5dfYoqmxmRecSPLy6UrrmqcCif3nkuQNSSB0JsIwIudGKc0QgiPKFnU1k94wYnMmdk\nOq9/+ywm5iRHvbY5rBIhBIpK2S0mXv3WPC6bmsMjH+5l0R8/4tXNBznUEIiAAKhr9ZBpbDCGR6K0\ntHvZfqgxFPv93C1z+NrZI8lKtIdcLeEC7vX52VfdQn2rm4ffLwIgKS4YhSJhctFIMP6uJJFn4CEC\nLnRiTHYiJgXbwxJ6/vaVQh65ruc6YsGyrYlG6F5RZTNn3vceW8ob+N1VU3jsSzM4UNvGbc9s4P2d\nlVQ2tWM1K+pa3dgsJjKctggLfMWeKnx+HeqvGSQnNY76Ng+5qXERLpTSujY8Pk1dq5sPdwWaYQRC\n5JS4CKLw9vYK7nltOyAW+EBEwgiFTjisZl755jyGZx6pCmi3mEOlbH//1i5WFFVHTQwK78YDRyzx\nYEGrheMHUZCVQFFlC1oHClgl2y3UG5EngWSeI+n0q4prjfsFRPq2Z9aT4bSTkxLH5rIGVvzw3IjP\nL6oMdKFPS7DRZHymUkrS6btgY2kdL64r45IpQ7oMHxViF7HAhahMyk3GabdQ2eRi+bbD3PPa9lDE\ngkkpNpXWR/3JneG0cXVhbmhDMt4I5Qtad0opFo4PVDpcb2yMnj9+EGePDkSrDEpyREShbDL6eaYa\npXRL69oorm4hNzWeQw1tERutENjADN6nuf2IYCfFWSUKJQrtnkAs/YNLpkUtOibENiLgQpdsKq3n\n7N98wK9e285/Nh7EZmTqjRuciF/DnsrONVMmDEnmN4unhKz1oH+1JSxNe6LRQPc1I6Pzq/NGcPm0\nQL/r8HR6n1+H6rIES8nGWU243D5yUuPw+DTffn5jxOcXVTaTlWgnNd4W8seDIeDSlacTgWYZ0g9z\noCICLnTJ+CFJjB+SRGltGzPyUkPZkWOyjU3OKEWvfH4dESNut5gwKSJqgrcZySMuI4Qtw2mjxuiv\nmZ3koKbFjdvrZ2NpXajAUlBkHFYzLq+P3JQ4AHZ3qJxYVNnMyEwnmYn2iFouSQ6LuFCi4PIENpkv\neXAF33luw8lejnCUiIALXWI1m3joC9PIS4sPddgByEuLJ85q7pSRCfDAu3sY/eM3Qq4NpRQ/vXg8\n547NCs05e3QmDy2ZhlkFfOQPvlfEOb/7AIBBSYFIlH3VLfzgX5tJclhJT7CRYAtY8g6LGZcnYIED\nmE1H/glrrdlb1UxBlpPbzxvFR/+7IHQuKc5Kkwh4J8xKkRxnxa91qJepMHCQTUyhWwYnx0UIIRCK\nBx+T3XnTq8nlxW4xhxpQAFw/d3jEnMxEOxdPGcKKomr217SSEm+lyeXF6/MzyIgFv+2Z9eyvbeWZ\nr57BGWF1TiYMSSLBbiHHsMAJKw1e1dROk8vLyMzIlmwgJWW74teLJwNw1SOfShTKAEQEXDgmfnrJ\n+Kjjze2eTv00S2tb0Rry0gN+8XX766hubufeKyahFCw1KgvWt3lCNbz3VDZz14VjI8Qb4FvnRbaR\n8/mOZBIWGRuYBVmJrN5Xy8PvF3HvlZPISYkLRaF0LJQlBIiziYtpICIuFOGYqWh0sXpfZJGqJpc3\nFEIY5JvPrOdny7aGjp9dfYC7/7sdk0mhlArFcde3uhmSHIfZpLhg/CBumT+C93dV8tWn11DfGlnv\nRGtNvM1MeBDKXiOEcGRWAg1tHj7cXUVtc+C65LhA44je9NM8nbj/7d089N4e4q1mXGKBDzhEwIVj\n5n9f3MzNS9dSVnck5b653Yuzg4AHGhsfEYeGNk+EyAdDBOtaPSTHW3nlm3N5YMk0lFKUVLfwzo7K\n0Ny/flTM2b99H6UUZ43KiPicospmEmxmspMcoV8BTUYoYSgbU9woEXy4u4rVJXWcMyazx0bWQuwh\nAi4cM7+4dAI+v+Zbz27AY7gyPjchmyuMkMAgTrslIoywsc0TqhAIgVKyP/jcGAYb/u+JOcmhqJOO\nUSitbh/7a1rx+TU5KfGU1bWFol72VrUwMsuJUir0gAiGEko9lOi0e3w4LCaunZXHdxeOPtnLEY4S\nEXDhmMnPSOC+/5nEhgP1XP7wJ/x7XRnXzR7Gl+fkR8yLt1kiNsgaXd6QoAIMSYnjmwsKQrHj4XSs\nVx2MB19/oI7/bj5Im8cXqh9eVNlMgVHEKiTgRvhispSUjUq714/deDh6fOJeGmj0q4ArpRYppXYp\npYqUUndGOW9XSj1vnP9MKZVvjOcrpdqUUhuN1yNh18xQSm0xrnlAyY7USeXiyUO4Zf4IWtq9tHbR\nFDfBbo6IA29s80QUoNJaU1rbSnVz54707R4fdosptPEYtMQP1LSGCmGV17VRUt3C4UYXI4N9MB2B\nDvbBPplJUlI2Ki7DAv/D27sZ/eM3OrW+E2KbfotCUUqZgYeBhUAZsEYptUxrvT1s2k1Anda6QCl1\nLfBr4Brj3F6t9dQot/4LcDPwGfA6sAh4o5++htALfnTROH500bguzy+eMZS5BUf81UtvmhXK6gyy\n4HcfcPP8Efxw0diI8USHJaJGR5wh4BVNR9LtN5fX89ePikmJt3KJ0eotNcHGe987JzQn2BdTXCiR\npMTbSHfacVjNaE2n1ndCbNOfYYSzgCKtdTGAUuo54DIgXMAvA35uvH8ReKg7i1opNRhI0lqvMo6X\nApcjAh7TzBiWCqSGjkdmRsaPK6VITbBR16GzDsBt547itnOPhA7mpcezaEJ2hEX/i2XbUQqeufmM\nUKhiR464UCRZJZw3vn0WAE9/WgIE9hxEwAcO/elCyQFKw47LjLGoc7TWXqABCAb+DldKbVBKfaiU\nOitsflkP9xRijMomF58V1+Dx+Wlz+3hixT72VERmceakxFFS09LjvWaPSOeRL83AbgmITLzVhNfv\n58El05gxLC1i7i1L14ZqggfT6sUHHp24UNExecANJGJ1E/MQkKe1ngbcATyjlEo6mhsopW5RSq1V\nSq2tqqrql0UKvWP5tgqueWwVda1uqpvbufvV7WwsrY+YMzEniW3ljZ2qC/76zZ3c9dLmTvccnOxg\nwZhMbl1QwB+umcoFEzqHwO2qaAo9KGwWE3FWs7hQwvD4/Hz5idW8tvlQqGqkNHUYWPSngJcDQ8OO\nc42xqHOUUhYgGajRWrdrrWsAtNbrgL3AaGN+bg/3xLjuMa11oda6MDMzsw++jnCsJAStu3ZfSECT\nOrRBmzgkmaZ2b0QbN4Ct5Q2hioQAGw7UMfXut8hJjePJG2Zx27mjuGxq9B9hTrslor6HpNNH4vL4\n+Gh3FQfr2xiVlcgt80fwjX+u561th0/20oRe0p8CvgYYpZQarpSyAdcCyzrMWQZ8xXi/GHhPa62V\nUpnGJihKqRHAKKBYa30IaFRKzTZ85V8GXunH7yD0AcGSss3t3pCAhocRApw1OpM/XTuVNKctYrzd\n4w9tXEKgDkt9q4fW9p4tRafdEmrqAIFkHrHAj9BuVIN0WE2MyU7kS7OHUVTZzJqS2h6uFGKFfhNw\nw6d9G7Ac2AG8oLXeppS6Wyl1qTHtcSBdKVVEwFUSDDWcD2xWSm0ksLn5da118F/VrcDfgCIClrls\nYMY4wUqCnxRVh3zQwczIIDkpcVw2NaeTsLd5fKHYbzgShfLVpWv5boda4B1JdFgia4I7rLKJGYbL\nCPu0W8z4/TpUS+ZQWEMNIbbp12JWWuvXCYT6hY/9NOy9C7gqynX/Bv7dxT3XAhP7dqVCfzJjWCoz\nhqXy+pZDLJmVB3TuJA+wu6KJAzWtnB9WutbliWw4EP4+2CGoK8ZmJxFvO/JPPC8tnle3HOKj3VXM\nH336udXavT5KqlsZk51oHBtJUlYTRVXN3PDkGoCIjkhCbCPVCIV+J85m5l9fm0Ntqxun3cLcgoxQ\n1cFwnvxkH29uPcz6nywMJe6MyExgRHhvzjBrPDnO1uke4Xz/c2Mijn988Xh2HG7iq0vX8rcvF552\nIv7S+nJ+8p+trP5/55OWYEMRKGOQGm+LcFOJBT5wiNUoFOEUw2RSZBgJI0PT4rGYO//TmzAkmbpW\nD+X1baGxR79UyA8+dyS5x2m3cKVRayWaFd8daQk2/vnVMxiZ6eSrS9eycm9Nr677tKiaFXuqj+qz\nYpHS2la8fk15XeDPd0Smk3fuOJv5ozNDUSgQqDLp80tG5kBABFyIGSbmJAOwtbyxyznxNgu/uiLg\nQQuWoe2KF9aWcu7vPwj5eiEg4s989QwGJdn57fKdPa7J4/Pznec38tNXtvY4N9YJlh441NDW6Vx4\n8o7Xr0Mt7oTYRgRciBnGZidiNim2HWwIjV384Mf8fdX+iHken+byqUMYN7j71ACXx0dxVUuooFWQ\n1AQbX5mTz/oD9eyu6NwWLpz3dlZS2dROcXULTQM8BLGqOSjgARfJuv21LP7LpxRVNuMwEqOCadDi\nRhkYiIALMYPDamZUlpMt5QEB9/r8bC1v7JRiv+B3H5DutHN2Dz7sYE3w5ii9Hq+YloPVrHh+TWmn\nc+E889kBgsUdth3s+pfBQOCIBe4KHa/dX4fH58dkUiTYzORnxBtzOlvpQuwhAi7EFA99YTp/unYa\ncKRrffgGGwSsxLYuKh+G47RHlpQNJ91pZ+H4Qby8obzLaJbS2lY+2lPFF4zImS1lDVHnDRSC1R4P\nG+IcXqrX7fXT6vFx1qjAQ1Es8IGBCLgQUxRkOUObk8G07vA4cICaFjfPfHagR/dHsP5JV93Wr5mZ\nR22Lm3e2V0Y9/8LaUhRw64IChiQ7Qr8MuuPdHRXc9dLmTiUBTjZ+v6baaC930BDn4IPLYTVT2eRC\naxiaGofNYpJQwgGCCLgQUzS0evjD27tZt7/uSKKJNXp1vAR791GwWUkBN0uCPfr18woyGJLs4Lk1\nBzqd8/j8PL+mlHPGZJGTEsfEnGS29kLAH/uomGdXl/LfzQd7nHsiqWt1hyJLguIcboEHx5Zvq2Bw\nskMs8AGCCLgQU9gsJp76tIS7XtpMu9fH3IJ0hiTHRZ2b0kMY4chMJ0/fOIvJuSlRz5tNiqsKh7Ki\nqJoDNZE1WN7YepjKpvZQ4tHk3GSKq1u6raXS0Oph7f46AH7z5q6I6JeTTdD6zk2N43CDC601qQk2\npuQmE2czRwh2dpJDfOADBBFwIaaIs5l5+AvT2VvVwr2v72TpjWcwr0Pz4mAziPg+qFt99cyh2Mwm\nlvx1FesM8X129QG+98JGRmU5WTAm4BMOhjhu6ybE8YPdlfj8mh98bgzl9W2hGtvhuL1+7n9r1wkP\n0wtuYE7OTcbt81PT4ubSKUN45bZ5xNssIcH2aS0W+ABCBFyIOeaNyuAXl07gvZ2V3Pv6jk7nFxfm\nkuG00U3vDyAglmf+37v87ePiLufkpMTxwtfmoBRc8+hKvvT4Z9z10hbOHJnBi18/M5RwNMkQ8C3l\n9V3e650dlWQ4bXzj7JEsGJPJQ+8XdYqg+aSomgfeK+LFdWVd3KV/qGoOCPKknMCvkY4+7kMNLswm\nhcfnZ3BKHBWNrpjz4wudEQEXYpLrZg/joknZPB6l+cOs/DSunZnX4z1sFhPVzW5qonT6CWfK0BRe\nu/0szh83iI/3VPP1s0fyxPUzSQ5LFEp32slJiWNLFxa4x+fng12VLBiThcmkuOuicbS0e3nIaCgR\nZFVxIPtzRdGJzeysbgr8GUzODTyIDjW4+PMHRSz+y6dAQNDjrGZa3T4GJzvw+HSPf27CyUdqoQgx\ny4NLpnPTvPqInpgAl0/rfRMmZ4eKhF2RHGflL9dNp6qpnawodVog0HRiS1l0C3xNSS1NLi/njQsU\n4ho9KJELJw3mPxvK+dFF4zCbAr8WVhoCvnpfbadCXf1JVXM7douJUcaf5aGGNsrr2thXHeiCdLDB\nRX56PDedNTxUPfJQQxuZifYTsr7ecP/bu5mWl8KCMVkneykxg1jgQsxiNilmDEvt0VXSHYGmDr3L\noFRKdSneEHCjlNS0Rt3IfHdHJTazibPC/PWLJmRT0+JmY2nAt97o8rC1vIHJucm0e/0hn/uJoKqp\nncxEOxlOO1az4lCDC5fHH3qAHG5oY9zgJK6YlstgY9M4lvzgbq+fh98v4oUeEq9ON0TAhVMap90S\nNZHnWJhkRLNsPFDPuzsq+NkrW1m26SBur593d1QwZ2Q6lU3tvL7lEABnj8nEYlK8bcSZr9lXi1/D\n7eeOwmJSJ9SNUt3cTobTjsmkGJTk4HCDi3avD7vFhMfnp7KpneR4K1vLG8hODjzEYikWfH9NCz6/\nDv1iEAKIC0U4pTlvXFZETfDjIbiRecNTa/D5NRaT4umV+0lLsFHb4uamecO55MEVNLd72f2rC0ly\nWJk9Ip13dlRw54VjWbm3BpvFxLxRGUzPS2XFnmp+uKhPltYjVU3tDE0LpMkPTnZwsL6NRIcVu9VM\nZVM7WkNJdQsXP7iC3b9chM1siikLvKgy0GyipKYFv19jMh37r7JTCbHAhVOa710whm+cM7JP7pWW\nYOPKaTksmpjN418pZNvdn+OpG2YyPS+FQUl2LpiQHbL2g0kz54/LoqiymX3VLazaV8P0vBQcVjPz\nRmWw9WBDpyiV/iLoQgHITo7jcKOLsdmJFA5LDaXWpzsD59t9fgYl22MqFjwo4C6Pn8ONsfNgOdmI\nBS4IR8H910yNOD5nTBbnGJtqbq8fq1lx07wRofKs540bxM//u52X1pex7WAj3zlvNABzCzK4/+3d\nfLq3hs9PHtwna/vhi5tJd9r430VjI8a9Pj+1rW4yDIEekuxg+TYX37tgNEopXjWyRrMMgW9z+xic\nFBdTFvgeQ8Ah8EthSEr05K7TDbHAhVOae17bzhn3vnNCPmtvVTMenybBZmav0V9yaFo8Y7MT+dvH\n+9AaZo9IA2BKbjKJdkuf+cHb3D5e3lDO05+WdMoArW1xozVhFrgDt9dPrWH9B33dg4wN3Fa3j+xk\nR0z5wIsqmxkzKNAKrlj84CFEwIVTGrPJRE2zO1SJrz9JS7Dxo4vG8uB7Rdz/1u7Q+MLxg2jzBDYM\np+YFNkItZhOzR6bziSHglY0uNhw49qiUDQfqcPv8tLh9vLsjsjhXpZGFmekMtKAbbGxS3vT0Wn7y\nn60crHcRbzOTlhCIe291+xic4gil3J9s/H5NcXUzcwsycFhNlIiAhxABF05pzh2bhdmkuPiBFawt\nqe3XzxqU5OCW+SOZPzoj5LMFON+IDS/MT8VuORL3PX9UBgdqW/lgVyWXPLSCK/786TEL5sriGswm\nRYbTxisbyyPOBR9eMn8cEgAAIABJREFUQQs8GCZYXt9Go8vD4cY2spMdTB2ayv1XT2FwsoPBSY5Q\nyv3Jpry+DZfHz6hBTvLTEyQSJQwRcOGUZtbwNF669UzsVhPXPraK1fv6T8TXH6ijsslFQVYixdXN\neH2Ban+TcpKZW5DOldNyI+YvnjGUsdmJfOf5jaGCXce6QbequIaJOclcNjWHD3ZV0dB6JFa9KmSB\nByzvoAXebvwqKK1tY3CygyEpcVw5PZfUBBvZwVjw+pPvRgk+DAuynAzPSGBfjQh4EBFw4ZRnwpBk\nlt02j2tmDg2lkpfVteIxBLYv0Fpzw5Nr+OM7exiV5cTj0+yvDVQ4NJkU//zqbP5nRqSAx9nM/OW6\nGfh8OpQcVFx19OLU5vaxsbSe2SPSuGzqENw+P29uOxQ6H2yllpEYcKGkO+1YTAq3109ZXRtbyhuY\nW5BBm9vH6n21VDe3M2FIEiZFTJTF3VMZKKVQkBkQ8AM1raGH4+mOCLhwWpAcZ+WeKybhsJpp9/pY\n8tdVjPnxG8y+912u/PMnvLn18HHd/2CDi4Y2D+MGJzFqUCBdfU9Fcw9XwfCMBO65YiJ7DeHuWPel\nN6zbX4fHp5k9Ip1JOcnkp8ezbNMR4a1qaifBZg7Fw5uNZB63z89nxTWcOTKdr80fycGGNq5+dCWf\nFFUzNC2ey6fmsHRlSZ/sH7S6vTS0HVtP0aLKZjKcNlITbORnJOD1a8rqYifE8WQiAi6cdlhNJv7f\nReO4bUEBZ43KoMnl5ev/WMdTn+w75ntuN/pljh+cxOhBiTz6pRnMzE/t1bWpCbbQ++fXlh61YK4y\n/N8z89NQSnHp1Bw+3VtDpeGOqW52k5lox+PzU2xExwxOduDXgaYYDyyZhtmkQuV5W41OSLedW4Db\n6+exj7qu5tgbtNZc/+QarvvbZ8d0fVFlMyMzAw/FERkJAOJGMehXAVdKLVJK7VJKFSml7oxy3q6U\net44/5lSKt8YX6iUWqeU2mL899ywaz4w7rnReEllG+GoMJkUiyYO5o4LxvDbq6bw2u1ncft5o1g0\nMRCP3ZsyqqW1rTS0eXhixT78fs32g40oBWOzE3FYzXxuQnYoMaYngpb6jy8ax96qFj7/wMcs23Sw\n10K+qriGSTnJoR6gl04ZgtbwL6NkbVWTiwynnXte28G5v/+QykYXOalxmE2Kx6+fGYoPj7cGrg8K\n+IhMJ5dNzeHvK/cflxX+6uZDrN5Xy5byo09c0lpTVNkcKmiWHxTwY3A1nYr0WyKPUsoMPAwsBMqA\nNUqpZVrr7WHTbgLqtNYFSqlrgV8D1wDVwCVa64NKqYnAciC8BN0XtdZr+2vtwumFzWLijoWBBBu/\nX3PDU2uYmZ/KN84pCFURDOdwg4vzfv8hYwcnsrmsgYxEO9sPNTA8PSHU5m37wUaKqpq5dMqQHj9/\nT2UzqfFWbjprONOGpfC9FzZx+7MbgIDFOSIzgawkB9lJDkZmOhk9yEl+RgJWs4lWt5dNZfXcNG9E\n6H4FWU7OGZPJIx/u5dqZQ6lqamf0oETe3l4BBOKo71g4miWz8piZnxa6zumwYDObqAjbSL3t3AJe\n2VjOox/u5UcXjTvqwmIuj4/73thJaryVulYPa0pquWBCdq+vr2pup9HlDQl4+v9v78zjo6yu//8+\nM5M9IXtCFkgCCUuAsAVEdsEVKOCuP6vWaqlbtdW2Wmv9WVu1Wq3fH2ptwRW/KnVDUUGroCyKQNiD\nQBYSQgIEQjayZ2bu74/nmWEmC4QlYMJ9v1555Zn7bPdyyZn7nOeczwnyJcTfRuEprsB37K/m4Y+y\nDdng41R2+jHTmZmYo4E8pdRuABFZCMwCPA34LOBRc/t94AUREaXUJo9jtgMBIuKnlDqzZUw05xyN\ndich/jae+W8OX+04yEPTBjI6JcLrmH+tyMepFC9cP4I5b2bx3Jc5zLtxJBUekR+LNhWzYM0epg+J\na/NLwJO8g0dIiw3hldUFPL5kB1seuZjcgzWsLywnq7CC4oo6NhZVuhNvwKhGdMWIBPrHhpj+b+8+\nPjx9IJf8zyqe+yqHspomxqX6ERXiR0llPfmHDJfEnAVZ/GlGOldn9gIM33if6CCvEMi+0cHMHBrP\n/FUFvLy6gEAfKxcMiOH564d3yJjPW7mbksp63rx1NLe+kXXCBtwzAgUMxciUqFMPJVyafYANeyrY\nsKecKQNivfZ1Ja2VzjTgCYCn9mMxcF57xyil7CJSBURirMBdXAlsbGG8XxMRB/AB8FfVRvCsiMwB\n5gD07n188X+NBozIkOevH85F6bE8/tkOrvn3Gib2i+bpKzPoGepPaXUDb68r4ooRCfSODOT+i/vz\niwVZbCyq4FqPIhNpMSE02p0UV9SRFBnU7v2UUuSU1jAjI46eof4oBcUV9YxMCmdkUjhMOnpsQ7OD\nvIM15B48wurcw7ybVUyT3en2f3uSGhPCjWOSWLCmEKeCqGA/Ft0xlr//dxeD40NpaHZQ3WCn5R/O\nozMHtVqR/nnWYAYnhFJd30zh4ToWb9nHhQNjj6vLXlJZz0vf5DNtSE8mpEUzLDHshMM4XQY8LSbE\n3ZYSFXTKUryb9xq67ttLqr0M+CMfZ7M6t4zlv518Stc/U/yotVBEZBCGW+Vij+YblFIlIhKCYcBv\nBBa0PFcpNQ+YB5CZmXn208k0XQYRYdawBC5O78mb3xeyaNM+wszqPPe/uwWHU3H3BWmAIVYVEeTL\nAx9s49JBce4qPqkekSjHNuDwxOVDiA/zdyf55B+qIT2+R6tj/X2sDE4IZXBCKJcPT+ShaQP4T9Ze\nfK0Wt+vGuKbi610HuWVsMos2lVBV30x0iCEl+4Cpk5Jnhua1LCgxpk9kq/uGBvhw2wTDReN0KgoP\n1/Lk0h1clB7rdV8XDc0O3viukBe+zkOh+MNlAwEjJv+lFfnUNtrbPK8t8g7WEOxnI7bH0fcJyZFB\nLN6y76QLYjidii0uA77vaIWlIw3NvJdVTH2zA7vD6S6n1x4FZbX8sK/6tGnZnAyd+RKzBOjl8TnR\nbGvzGBGxAaHAYfNzIrAIuEkple86QSlVYv4+AryN4arRaE47Ab5W5kzsy5J7xuPvY0UpQ4/66pGJ\n9I40pFlFhPk3ZTKsVxh4PHW7Hvk9RZjawmIRpmfEMbx3OClRQYh0PBY8MtiPOyenuo0rGMb7iSU7\n+PnrWby8uoBfX2h80dQ12rn19fVkl1SxY381Dc1GHLW/zdsEVNU1s2hTMfsq2w7Ts1iER2cOorS6\nkRdblIsD+HrnQaY+u4Inl+4kMymcxXePd8vYjkqJwOFUbCpqv66oJ012J9/sOkR6XA8vd02f6CCU\nMl4knwwFh2upqm/G12Yhe1+Vuz2ntIZ6U0emI0JeD7y/lbve3sjn2fuPe2xn0ZkGfD2QJiIpIuIL\nXAcsbnHMYuBmc/sqYLlSSolIGPAZ8KBS6lvXwSJiE5Eoc9sHmAFkd+IYNBq38RARPrprHI/NGuy1\nf2RSOB/dNc7L9dDD34eePfzdSSjtkVN6hPWF5TidigBfK/GhAewuO378eFsopfjzJz8wf5URDtkj\nwMZN5yfzr5+OxGoVlu08yII1hUyfu8qdOOTXYgV7qKaB3/xni7t2Z1uM6B3OFSMSeHlVAdklVTTa\nHVTWNXHfu5u55fX1BPpaeeu283jtltH0iw3xOC8Mi8C6gqPXPlDV0G5Szttr91BUXscdF3jLASeb\nTzT5JxmJstn8Apk+JI7iinp31mq+x5ft3opjfznklB5hXWE5/j4Wfv/+VoqPc3xn0WkGXCllB+7G\niCDZAbyrlNouIo+JyEzzsFeASBHJA+4DXKGGdwOpwCMtwgX9gC9EZCuwGWMFP7+zxqDRtCQ6xA9f\nW8f+bN76xXmtjH1LFqwp5NbX1+NaYP5yUh8uTu/4Sz5Pnv5iF69/V8gvJqRQ8OQ0fnfJAKwW4dLB\nPSksqyPYz8a41CicCqrrm7lqZCIJYd4l5JIig/CxynGfHB68dAA+VmHG86vp//DnjPjLl3y8eR+/\nmpLKp/eMZ1xqVKtzQvx9GBQfyjpTk2Z1bhlj/7aM2f/8luySKq9jqxuambs8j7F9I5ncL9prX1ps\nML5WSyttG6dTtVJibIvNeysJ8rUyc5gRIbTdXIW7FCTh+Kv7t9cW4Wu18M4vxuBU8OuFm89Kdmin\n+sCVUkuAJS3aHvHYbgCubuO8vwJ/beeyI09nHzWazsKVfFJYVsuB6oY2/cu5pTWkxYa4V/k3nZ98\n0ve764JUxvaNZHxqlPt6roiK3INH6Bsd5O6TUvDM1UNbXcPHaiElKsgrizSn9Ai3v7mBv8we7DbM\nMT38WXTXONYVlFNV30xNo50ZGXEMig89Zh9HJUfw1to97C2v496Fm+gVEUhpdSMzX1jNz8el8MtJ\nfYkO8ePfK/Ipr23iD5e1Dl0M9LVxXp8Ilu86yMMz0t3tL36dxxtrCvnqvkmEBfrSHpv3VjK0VxgZ\nZoWl7fuqGZsaZSYMBXFen0iSIoK48ZW1zBwa747ScVHXZOeDDcVMG9KT4b3Defzywdy7cDMvfp3P\nvabL6kyhMzE1mk7mTx9n8/PX17N02/5WaoN5B2vcleIBmh1O8g7WUNd04nU8g/1sTEiLRkSobbQz\n8emveWV1gfs+qTEhpES53A/tr7DTYkLcLzmb7E6e/nwnu8tqW0V+9IsN4adjkrjrglQeuHTAcY03\nwOiUcBrtTq6b9z31zQ5euTmTr+6bxLWjevPy6gLG/m0Zv3pnE6+sLmDWsHiGJLZ9zSkDYth9qJY9\nZjy4UoqF6/dSVtPEv1Z4Z45W1Te7KyQ1NDvYsb+aYb3CiAz2Iy7U3+0H311Wy4CePXji8iH42IRV\nuWW8+HVeqzn7ZMs+jjTauWFMEgCzhiVwUXosb6wpPK36Oh1BG3CNppN59pqhJIYHcMdbG5k2dzVL\nt+2n2eHkcE0jh2ub3C88AdYVlHPhP1a4/bQd5YklO7wqtgf52VAoNuypoMnupHdEIEN7hRLkZyMu\n1J9nv8wh9aElbgPoSVpsMHvK62hodrCxqIKvTH3xjaegV+7CFe5YUlnPk1cMITUmhNAAH568YgjL\n7p/EDecl8c3OgygFv724f7vXmTLASMBevvNo30oq64nt4cfr3xW4ZQRyS48w/qnl3LvQSC3Zvq8K\nu1MZL52BQfE93JEoS+6ZwKMzB+FwKt7LMrJYCw/X8f1ub1fN/35fRL/YYDKTjkolXJPZi/LaJlbn\nnrlC1aANuEbT6cSE+LPkngn845qhNDY7uOOtjazKPXQ0xtnjRV+f6OOvkFtSVdfMa98WkNfinJG9\nw9lQVIGPVXjv9rFu98yfZw7iulG9sTtVm/78n45J4vs/TMXPZmF1bpmhp54Rx8Y9FR2SGTgWFXVN\njO0bwW3jU5g1zDuOvG90MI/OHMT3D01l+W8nu6NX2iIp0shQdRnwjzfvw89m4dWfjcLuUDy/PI+K\n2iZufSOL+iYHn27dz9Jt+90RMK7CGoPiQ8k/ZDzxBPhaiQ7x46EPt/Fu1l4m9osmxN/GwvVF7vtu\nLKpgW0kVPx2T5OXamdQvmrBAHz7a3DLQrnPRBlyjOQPYrBauGJHIl/dNYv5NmZzfJ4qMxDA+uON8\nRpjGBKBnD38Cfa0nFGHxxfYDNDsUM1rEI49ICufQkcZWyn0XD+pJfzNO3bPAhIuoYD9ie/gjIqzK\nK2NYrzAm9YumusF+0hEyADWNdn6xYANV9XZ+NTWNIw1tqxMG+dlIaKPmZUllvbtoNMCU/jGs3W34\n4D/bup8L02MZFB/KtaN68c66In72+noOVDewcM4YhiSE8qePs1mRc4iEsABiQoyXt4Pie6AUfLix\nhCeW7KCqrhkRcCr4SUYclw9PYGn2ASrrmmhodvDgB1uJCvZrlcTka7MwbUgc/91eSm3jibu/ThZt\nwDWaM4jVIlyUHkuAr5UAXysjkyII8T8afigi9IsNYcGaQvdj//H4ZOs+kiIDGZLg7S8e0dt4xL/p\n1XVc/s9v3b7citomt9ysv0/bJmD+yt28s66IbcWVjE+N4ryUSK4amYhXsPsJoJTioQ+3sedwLXdO\nTmX041+xcN3eNo99cskOXvom36vN7nAy7m/LufGVo4qGUwbE0ORw8tTnOzlc28Rsc0V/z9Q0bFZh\ny95KnrpyCJnJETx1ZQaVdc2syi1zu08ABpv/Zst2ljJ/1W78fCzuGPi02BCuHdWLJruTRZsMA59T\nWsOz1wylh39r/ZTZwxKob3a4NWfAkPJdkXOI+St389v3ttBkP70+8h91JqZGcy4y97rhvL9hLwGm\nfrfTqfi/i7cz1cz6bHY4sVksDIgL4UiDne/yD3P7pD6tojUG9AzhhvN6s2TbfvxsFvf+7H1VbDRd\nCW2twMGQtW12OEmLCWFCWhS9IwPbjFrpCJ9n7+fDjSX894dSfndJf6ZnxDF/1W7e31DMbRNSvPpt\ndzh5e10RUwfEUFrd4C60vNUMM9xUVOmOrMlMjiDYz8bba4sIDfBhkhluGNvDn6euzKCuycHlZhWk\n9Pge3HlBKnOX5XoZ8LhQf8IDfcg/WEtieAC+VgvbzHsdrmlk6sBYMhJDeX55HuW1Tdw2PsV9n5Zk\nJoWTEBbAR5tLmDUsnn+t2M3fv9iJy+sUE+JHaXXDMV1DJ4o24BrNj4zekYHc5/ECb0+5oT/y5vd7\nvI57bNYgxvaN5Pw+kfykDdVDm9XC45cPYWXuIa8Xpa5QQl+rpV2hrbSYYHYdOMIXv5noblPKKKTQ\nlgFSSnkZYqUUjXYn/j5WCg/XsX1fNVePTOSOSUZSzpUjE/nTR9ls31ftXgUDbCmu5EiDnY8276O+\n2cG/b8wEYFWO8XLwwzvHuoWmfG0WJqRFsTT7ANOG9PTy57f0rwPcfUEqAT6GCJgLEWFwQihrC8oZ\nnRxB1p4KtyiZKxb8ulG9eWjRNgbF9+B3l7b/YtViEWYOi2feyt3MeXMDX/5QyrQhPblxTDL9e4YQ\nEdR+aOPJog24RvMjJyUqiLUPTeX73YdxOBU+Vgu1jXaG9Q4jLjSA/72tpUbcUeqa7Owtr+cnGUcN\nfM8e/gT4WLl+dPsib2kxwSzNPkCj3eFepc9buZsnl+5k8yMXecVZF1fUcdVLa/jZuGRuNw30Z9v2\n88RnO3hnzhhun9TX3e7iJxlx/OWTH3h/Q7GXAV+ZU4ZF4KL0WNbkG+O1WoTv8svISAx1u4VcXJQe\ny9LsA273ybHwtVm4Y3LfVu3npUSwKreM1Xll7DxwBF+rcOfkVEYmGREzs4fHU3i4lhvO693uE4uL\n2cMSeOmbfJbtKOXBywbwy4mtn4xOJ9qAazRdAH8fK5P7n3jtkk9MX7fn6s9iERLCA9hVWt3eaW4D\n/cZ3hcyZaBi9jETD9bCpqJILzDA+pRQPf5TNgeoGt3+30e7gqc930iPAh8Twtt0FYYG+XJgew+It\n+3h4+kC3cNTK3ENkJIYxbUgcX2wvZfu+KjISw3jtllGUVhv+5A17Ktz67bOHJZASFcTw3h2rftQW\n12T2Yt7K3WQmR1BW08gVIxL4tXl9MBKHHpo2sEPX6t8zhIenD2RgXI82s1FPN9qAazTdmKtGGlmE\nLl+wi5KKei/d75bMHp5A1p5yr/OG9grFahE2FlW4DfgnW/fzza5DPDIjnZ+PTwEMo7+3vJ4FPx99\nTC3031zYD4tF3Mbb6VQkhgeSkRDK+X2NrNVv8w6TkRhGoK+NlCgbizaV8MLyXG4Zm0x4kC8Wi5yS\n8QYjq3Tro5d46YCX1zaxr7Le6+mgJUu2GZWGHpmR7qUf7iku1tloA67RdGOsFvHSKXfx7YNTqD5G\nkeGIIF/+eYO3akWgr42BcSF8sf0APxkaT0yIH499sp2MxFBuHpsMGEbtiSU7mdgvmontvOxz4Yp/\nV0rR5HDiZzO02F30iw3mu/wyfKyC3am4fVJfLugfzdxluazMPdSmn/tkqGm0E+xn8zLCc5fl8l7W\nXrL/fEmbLpDaRjt/XLSNirpmkiIDuWVcymnpy4miwwg1mnOQCLPC+4nys7EpFJXXkVNqpNpP6hfD\nk1cMca+0o4L9jJjr6R1zOSiluPOtjTz0YXarqvV/nJ7O/Rf3583v97DeLASRkRhGeKAPK3YdAqC+\nycF3+WWt0t07yu5DNUz++zcs2lTs1d4rIpDaJodXlSVPgvxsvHf7WManRvHU5zvdxaLPNNqAazSa\nDnPVyETW//FCLk7vSVigL89eM9RLA2V0SgSf/Gq8V3bpsRARUmOC+WBjMWOeWMafPjqqDj2pXzQR\ngb7sOVzHhDTDn2y1CJP6RbMi5xDNDieX//Nb/s/8tTz9xa4THsvB6gZuenUdSimG9fJ2w/QKNxKJ\nilqoErqKZSilSI0J5h/XDMXPZuX+97a49VbOJNqAazSaEyLE36fDkrod4e4pqfSPDaG+2cHAOO9K\nRI99apTQneDhjpncP4bQQB/Kahq5ZVwylw3uyUvf5PPatwUdvmdpdQM3v7ae8tomXrtllFvky4Wr\nYEdLWdnnvszhltfW8435BBDTw5+/zB7MaLNYxZlG+8A1Gs1Zxc9m5blrh/HYp9u5KN27wPBXO4ys\nxj4eBnbWsHh3Kvu1o3pz1che3PnWBh779AdSY4KZkBbNq6sLWLxlH9eN6sXMYfEE+h41dZV1TVzw\nzDfYHYqXb850R9d40suMnqmsO1pI+uVVu5m7PI+rRyYyuf/RL5SZQ+OZ2UYc/plATtZ31JXIzMxU\nWVlZZ7sbGo3mBMkpPUJDs6NNI+tJQ7ODm15Zx68vSmNs3yg2763kd+9tIfdgDSF+NqJC/Aj0tfLZ\nPRMA+M/6Is7vE+VeabfFE0t28MClRlGM55fl8uyXOUwb0pPnrx/RZnTNhj3lPPNFDvNvzsRmEZxK\nEehrY/PeSvrHhhDge+L1OwFEZINSKrPNfdqAazSa7kCzw0lto90dw66UIaf7XlYxtU124kL9+eP0\n9ONcpTXVDc2MfvwrRqdEMv+mke0m86wrKOe6eWuYkRHPoSON+NosPHP1UCY+/TWZyeG8fHPmcROB\n2kIbcG3ANRrNSdJod7Ayp4wJaVH4+xzbAP/jyxzmLsvFahGevXoos4cn8O76vfz+g61ck5nI01ed\nuJ7MsQy49oFrNBrNMfCzWVv55tvjnimp1DXaGZsayZQBxjnXjDKSqUYkHdsNdDJoA67RaDSnCZvV\n4lWn04XLiJ9udBihRqPRdFG0AddoNJouijbgGo1G00XRBlyj0Wi6KJ1qwEXkUhHZJSJ5IvJgG/v9\nROQ/5v61IpLsse8PZvsuEbmko9fUaDSac4VOM+AiYgVeBC4D0oHrRaTl69lbgQqlVCrwHPCUeW46\ncB0wCLgU+KeIWDt4TY1Gozkn6MwV+GggTym1WynVBCwEZrU4Zhbwhrn9PjBVDPHdWcBCpVSjUqoA\nyDOv15FrajQazTlBZxrwBGCvx+dis63NY5RSdqAKiDzGuR25pkaj0ZwTdNtEHhGZA8wxP9aIyIkL\nBkMUUHb6evWjozuPrzuPDfT4ujonMr6k9nZ0pgEvATzTjxLNtraOKRYRGxAKHD7Ouce7JgBKqXnA\nvJPtPICIZLWnQdAd6M7j685jAz2+rs7pGl9nulDWA2kikiIivhgvJRe3OGYxcLO5fRWwXBnqWouB\n68wolRQgDVjXwWtqNBrNOUGnrcCVUnYRuRv4ArACryqltovIY0CWUmox8ArwpojkAeUYBhnzuHeB\nHwA7cJdSygHQ1jU7awwajUbzY+ackJM9WURkjumK6ZZ05/F157GBHl9X53SNTxtwjUaj6aLoVHqN\nRqPpomgD3gbdLV1fRHqJyNci8oOIbBeRe832CBH5UkRyzd/hZ7uvp4KZrbtJRD41P6eYEg15pmSD\n79nu48kiImEi8r6I7BSRHSJyfneaPxH5jfl/M1tE3hER/648fyLyqogcFJFsj7Y250sM5prj3Coi\nIzp6H23AW9BN0/XtwP1KqXRgDHCXOaYHgWVKqTRgmfm5K3MvsMPj81PAc6ZUQwWGdENX5f8Bnyul\nBgBDMcbZLeZPRBKAe4BMpdRgjACF6+ja8/c6hgyIJ+3N12UYkXZpGLkrL3X0JtqAt6bbpesrpfYr\npTaa20cw/vgT8JYyeAOYfXZ6eOqISCIwHXjZ/CzAFAyJBujC4xORUGAiRtQWSqkmpVQl3Wj+MCLi\nAsx8kEBgP114/pRSKzEi6zxpb75mAQuUwfdAmIjEdeQ+2oC3plun65uKj8OBtUCsUmq/uesA0LHC\nfz9O/gf4PeA0P0cClaZEA3TteUwBDgGvmS6il0UkiG4yf0qpEuAZoAjDcFcBG+g+8+eivfk6aZuj\nDfg5hIgEAx8Av1ZKVXvuMxOoumRIkojMAA4qpTac7b50EjZgBPCSUmo4UEsLd0kXn79wjFVoChAP\nBNHa/dCtOF3zpQ14azoiAdDlEBEfDOP9llLqQ7O51PWoZv4+eLb6d4qMA2aKSCGGy2sKhs84zHwk\nh649j8VAsVJqrfn5fQyD3l3m70KgQCl1SCnVDHyIMafdZf5ctDdfJ21ztAFvTbdL1zf9wa8AO5RS\n//DY5SllcDPw8Znu2+lAKfUHpVSiUioZY76WK6VuAL7GkGiArj2+A8BeEelvNk3FyFLuFvOH4ToZ\nIyKB5v9V1/i6xfx50N58LQZuMqNRxgBVHq6WY6OU0j8tfoBpQA6QD/zxbPfnNIxnPMbj2lZgs/kz\nDcNPvAzIBb4CIs52X0/DWCcDn5rbfTA0dPKA9wC/s92/UxjXMCDLnMOPgPDuNH/An4GdQDbwJuDX\nlecPeAfDn9+M8QR1a3vzBQhG5Fs+sA0jGqdD99GZmBqNRtNF0S4UjUaj6aJoA67RaDRdFG3ANRqN\npouiDbhGo9F0UbQB12g0mi6KNuAajYmIOERks8fPaROHEpFkT2U6jeZ00G2r0ms0J0G9UmrY2e6E\nRtNR9ApcozkgfZE4AAABfklEQVQOIlIoIk+LyDYRWSciqWZ7sogsNzWcl4lIb7M9VkQWicgW82es\neSmriMw3da//KyIBZ21Qmm6BNuAazVECWrhQrvXYV6WUGgK8gKF8CPA88IZSKgN4C5hrts8FViil\nhmJolrgKb6cBLyqlBgGVwJWdPB5NN0dnYmo0JiJSo5QKbqO9EJiilNptioIdUEpFikgZEKeUajbb\n9yulokTkEJColGr0uEYy8KUyxPwRkQcAH6XUXzt/ZJruil6BazQdQ7WzfSI0emw70O+gNKeINuAa\nTce41uP3GnP7Owz1Q4AbgFXm9jLgDnDX6Qw9U53UnFvoFYBGc5QAEdns8flzpZQrlDBcRLZirKKv\nN9t+hVEl53cYFXNuMdvvBeaJyK0YK+07MJTpNJrTivaBazTHwfSBZyqlys52XzQaT7QLRaPRaLoo\negWu0Wg0XRS9AtdoNJouijbgGo1G00XRBlyj0Wi6KNqAazQaTRdFG3CNRqPpomgDrtFoNF2U/w84\nw30H2HgCAwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oIVPvABi1D-p",
        "colab_type": "code",
        "outputId": "6dc669a5-604e-417d-f2ba-7941839c4936",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "loss, accuracy = model.evaluate(test_ds)\n",
        "print(\"Accuracy\", accuracy)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7/7 [==============================] - 0s 3ms/step - loss: 0.0285 - accuracy: 0.9850\n",
            "Accuracy 0.985\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_SznW9LD1jfL",
        "colab_type": "code",
        "outputId": "d29de94d-bfb5-4518-84ab-448b67c1fef6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "predict = model.predict_classes([40])\n",
        "class_val = predict[0][0]\n",
        "if class_val == 0:\n",
        "  print(\"you are fail\")\n",
        "else:\n",
        "  print(\"you are pass\")"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "you are pass\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-e45rGnW5oOd",
        "colab_type": "code",
        "outputId": "5fd57201-8c0f-4a6f-c0ab-2f0e18937202",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "predict = model.predict_classes([9])\n",
        "class_val = predict[0][0]\n",
        "if class_val == 0:\n",
        "  print(\"you are fail\")\n",
        "else:\n",
        "  print(\"you are pass\")"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "you are fail\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8muPk7JJ6L3r",
        "colab_type": "code",
        "outputId": "8af2fd44-dd7b-458e-e8b8-dab112d14860",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "predict = model.predict_classes([38])\n",
        "class_val = predict[0][0]\n",
        "if class_val == 0:\n",
        "  print(\"you are fail\")\n",
        "else:\n",
        "  print(\"you are pass\")"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "you are fail\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ts3HP_7ebHdF",
        "colab_type": "code",
        "outputId": "72a12dab-23cd-4722-d7cc-6cbdaff8d98a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "predict = model.predict_classes([39])\n",
        "class_val = predict[0][0]\n",
        "if class_val == 0:\n",
        "  print(\"you are fail\")\n",
        "else:\n",
        "  print(\"you are pass\")"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "you are pass\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}