Transfer_learning_with_NLP_USE.ipynb

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    "colab": {
      "name": "Transfer_learning_with_NLP - USE.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anshupandey/Natural_language_Processing/blob/master/Transfer_learning_with_NLP_USE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idEyIgYm0vPY"
      },
      "source": [
        "# Transfer learning using Universal Sentence Encoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJH1sjyl0od3"
      },
      "source": [
        "## Import packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wFyj25qUiMd"
      },
      "source": [
        "# import the packages\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import re"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPfAFCyY04C5"
      },
      "source": [
        "## Downloading the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X97nqrJYUpIi",
        "outputId": "5b4b6017-e5af-479a-f5d6-07750bb199e6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model_url = \"https://tfhub.dev/google/universal-sentence-encoder-large/5\"\n",
        "embed = hub.load(model_url)\n",
        "\n",
        "\n",
        "# Compute a representation for each message, showing various lengths supported.\n",
        "word = \"Elephant\"\n",
        "sentence = \"I am a sentence for which I would like to get its embedding.\"\n",
        "paragraph = (\n",
        "    \"Universal Sentence Encoder embeddings also support short paragraphs. \"\n",
        "    \"There is no hard limit on how long the paragraph is. Roughly, the longer \"\n",
        "    \"the more 'diluted' the embedding will be.\")\n",
        "messages = [word, sentence, paragraph]\n",
        "messages"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Elephant',\n",
              " 'I am a sentence for which I would like to get its embedding.',\n",
              " \"Universal Sentence Encoder embeddings also support short paragraphs. There is no hard limit on how long the paragraph is. Roughly, the longer the more 'diluted' the embedding will be.\"]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OoAJIjRYBg8H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26ced654-9a79-4fcf-866e-9d3bbab361e0"
      },
      "source": [
        "embed(messages)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3, 512), dtype=float32, numpy=\n",
              "array([[-0.03458562, -0.01799013,  0.00198058, ...,  0.11107819,\n",
              "        -0.03549766,  0.05680299],\n",
              "       [ 0.0583339 , -0.08185012,  0.06890938, ..., -0.00923877,\n",
              "        -0.08695352, -0.01415744],\n",
              "       [-0.02646177, -0.01911565, -0.04207037, ..., -0.00689673,\n",
              "         0.01810369, -0.02591053]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9bw3O6v-kcP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e380959b-9058-46ad-8f0e-93b48676e383"
      },
      "source": [
        "embed([word])"
      ],
      "execution_count": 4,
      "outputs": [
        {
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              "         1.10382855e-01,  1.55771106e-01, -5.95676564e-02,\n",
              "         1.12963468e-02,  4.71027847e-03, -2.25300994e-02,\n",
              "        -1.13187119e-01, -2.40785498e-02, -5.39944582e-02,\n",
              "        -3.50812413e-02, -5.43226413e-02,  5.87365925e-02,\n",
              "        -1.45391263e-02, -3.59623171e-02,  2.98933666e-02,\n",
              "         8.83317068e-02,  1.22067695e-02, -1.99052244e-02,\n",
              "         1.32855326e-01, -2.32943315e-02, -3.45242955e-02,\n",
              "         1.58798285e-02,  5.18729128e-02,  3.51572037e-02,\n",
              "        -4.24405979e-03,  1.07904617e-03, -3.74839045e-02,\n",
              "        -3.27034034e-02, -2.80124806e-02, -5.22373728e-02,\n",
              "         3.63478884e-02, -2.67687477e-02,  1.11078210e-01,\n",
              "        -3.54976952e-02,  5.68030216e-02]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddWa1eJLFK8W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8323a4d6-a987-43e6-897c-0aa5f621cd15"
      },
      "source": [
        "for doc in messages:\n",
        "  print(doc)\n",
        "  print(len(embed([doc])[0]))\n",
        "  print(\"\\n\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Elephant\n",
            "512\n",
            "\n",
            "\n",
            "I am a sentence for which I would like to get its embedding.\n",
            "512\n",
            "\n",
            "\n",
            "Universal Sentence Encoder embeddings also support short paragraphs. There is no hard limit on how long the paragraph is. Roughly, the longer the more 'diluted' the embedding will be.\n",
            "512\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBTwOs4JVDRt"
      },
      "source": [
        "#size of embedding\n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZpdFlvTM2jis"
      },
      "source": [
        "## Download data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "py5FUAQtVpwE"
      },
      "source": [
        "!wget -q https://raw.githubusercontent.com/Tony607/Keras-Text-Transfer-Learning/master/train_5500.txt\n",
        "!wget -q https://raw.githubusercontent.com/Tony607/Keras-Text-Transfer-Learning/master/test_data.txt"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1B2eIRUj3X-3"
      },
      "source": [
        "## Decscription of Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXpR6dEE3a6r"
      },
      "source": [
        "The dataset we use is the TREC Question Classification dataset, There are entirely 5452 training and 500 test samples, that is 5452 + 500 questions each categorized into one of the six labels.\n",
        "\n",
        "- ABBR - 'abbreviation': expression abbreviated, etc.\n",
        "- DESC - 'description and abstract concepts': manner of an action, description of sth. etc.\n",
        "- ENTY - 'entities': animals, colors, events, food, etc.\n",
        "- HUM - 'human beings': a group or organization of persons, an individual, etc.\n",
        "- LOC - 'locations': cities, countries, etc.\n",
        "- NUM - 'numeric values': postcodes, dates, speed,temperature, etc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6NTBzTaV3a9"
      },
      "source": [
        "#Extract lines from .txt and convert to dataframe\n",
        "\n",
        "def get_dataframe(filename):\n",
        "    lines = open(filename, 'r').read().splitlines()\n",
        "    data = []\n",
        "    for i in range(0, len(lines)):\n",
        "        label = lines[i].split(' ')[0]\n",
        "        label = label.split(\":\")[0]\n",
        "        text = ' '.join(lines[i].split(' ')[1:])\n",
        "        text = re.sub('[^A-Za-z0-9 ,\\?\\'\\\"-._\\+\\!/\\`@=;:]+', '', text)\n",
        "        data.append([label, text])\n",
        "\n",
        "    df = pd.DataFrame(data, columns=['label', 'text'])\n",
        "    df.label = df.label.astype('category')\n",
        "    return df"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0KFQSdpSWEB7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4069040-2202-4f7e-f931-bb1f440633d5"
      },
      "source": [
        "#Assign train data\n",
        "df_train = get_dataframe(\"/content/train_5500.txt\")\n",
        "df_test = get_dataframe(\"/content/test_data.txt\")\n",
        "\n",
        "print(df_train.shape)\n",
        "print(df_test.shape)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5452, 2)\n",
            "(500, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxH0qnjIGl86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "e8b2c3e1-2490-4d68-8292-1d48b23e575d"
      },
      "source": [
        "df_train.head(10)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  label                                               text\n",
              "0  DESC  How did serfdom develop in and then leave Russ...\n",
              "1  ENTY   What films featured the character Popeye Doyle ?\n",
              "2  DESC  How can I find a list of celebrities ' real na...\n",
              "3  ENTY  What fowl grabs the spotlight after the Chines...\n",
              "4  ABBR                    What is the full form of .com ?\n",
              "5   HUM  What contemptible scoundrel stole the cork fro...\n",
              "6   HUM  What team did baseball 's St. Louis Browns bec...\n",
              "7   HUM                    What is the oldest profession ?\n",
              "8  DESC                           What are liver enzymes ?\n",
              "9   HUM  Name the scar-faced bounty hunter of The Old W..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9ad70e30-9aeb-47fb-9df3-827a652e45ed\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DESC</td>\n",
              "      <td>How did serfdom develop in and then leave Russ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ENTY</td>\n",
              "      <td>What films featured the character Popeye Doyle ?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DESC</td>\n",
              "      <td>How can I find a list of celebrities ' real na...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ENTY</td>\n",
              "      <td>What fowl grabs the spotlight after the Chines...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ABBR</td>\n",
              "      <td>What is the full form of .com ?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>HUM</td>\n",
              "      <td>What contemptible scoundrel stole the cork fro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>HUM</td>\n",
              "      <td>What team did baseball 's St. Louis Browns bec...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>HUM</td>\n",
              "      <td>What is the oldest profession ?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>DESC</td>\n",
              "      <td>What are liver enzymes ?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>HUM</td>\n",
              "      <td>Name the scar-faced bounty hunter of The Old W...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ad70e30-9aeb-47fb-9df3-827a652e45ed')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9ad70e30-9aeb-47fb-9df3-827a652e45ed button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9ad70e30-9aeb-47fb-9df3-827a652e45ed');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTwA-WJbWIC0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f38e50b0-5307-450e-b9f5-cd5d36670f51"
      },
      "source": [
        "#Number of categories in dataset\n",
        "category_counts = len(df_train.label.cat.categories)\n",
        "category_counts"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XRwKqA4WK_4"
      },
      "source": [
        "#Convert input into strings  \n",
        "def UniversalEmbedding(x):\n",
        "    return embed(tf.squeeze(tf.cast(x,tf.string),axis=1))"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s8EZZTRwQLQe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4aed959d-0afb-493f-f3ed-30f53c05652c"
      },
      "source": [
        "UniversalEmbedding([['hello']]).shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([1, 512])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_haacVQ4w7d"
      },
      "source": [
        "## Model Building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRSXzOUs1BS0"
      },
      "source": [
        "from tensorflow.keras import models,layers"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zisA3Rn3G71_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1bb15ea7-7859-40bf-f17f-6492d52ea6e7"
      },
      "source": [
        "input_layer = layers.Input(shape=(1,),dtype=tf.string)\n",
        "hub_layer = layers.Lambda(UniversalEmbedding,output_shape=(512,))(input_layer)\n",
        "dense1 = layers.Dense(256,activation='relu')(hub_layer)\n",
        "output_layer = layers.Dense(6,activation='softmax')(dense1)\n",
        "\n",
        "model = models.Model(inputs=[input_layer],outputs=output_layer)\n",
        "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:\n",
            "The following Variables were used a Lambda layer's call (lambda), but\n",
            "are not present in its tracked objects:\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_29:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_28:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_27:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_26:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_25:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_24:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_23:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_22:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_21:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_20:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_19:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_18:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_17:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_16:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_15:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_14:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_13:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_12:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_11:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_10:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_9:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_8:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_7:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_6:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_5:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_4:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_3:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_2:0' shape=(17,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_1:0' shape=(18,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/bias/part_0:0' shape=(18,) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/kernel/part_29:0' shape=(34, 512) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/kernel/part_28:0' shape=(34, 512) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/kernel/part_27:0' shape=(34, 512) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/kernel/part_26:0' shape=(34, 512) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/kernel/part_25:0' shape=(34, 512) dtype=float32>\n",
            "  <tf.Variable 'EncoderTransformer/hidden_layers/tanh_layer_0/dense/kernel/part_24:0' shape=(34, 512) dtype=float32>\n",
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            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_22:0' shape=(10, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_21:0' shape=(10, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_20:0' shape=(10, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_19:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_18:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_17:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_16:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_15:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_14:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_13:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_12:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_11:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_10:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_9:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_8:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_7:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_6:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_5:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_4:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_3:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_2:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_1:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_1/dense/kernel/part_0:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_29:0' shape=(10, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_28:0' shape=(10, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_27:0' shape=(10, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_26:0' shape=(10, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_25:0' shape=(10, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_24:0' shape=(10, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_23:0' shape=(10, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_22:0' shape=(10, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_21:0' shape=(10, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_20:0' shape=(10, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_19:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_18:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_17:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_16:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_15:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_14:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_13:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_12:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_11:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_10:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_9:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_8:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_7:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_6:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_5:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_4:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_3:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_2:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_1:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'EncoderDNN/DNN/ResidualHidden_0/dense/kernel/part_0:0' shape=(11, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_29:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_28:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_27:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_26:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_25:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_24:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_23:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_22:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_21:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_20:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_19:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_18:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_17:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_16:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_15:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_14:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_13:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_12:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_11:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_10:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_9:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_8:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_7:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_6:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_5:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_4:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_3:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_2:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_1:0' shape=(13334, 320) dtype=float32>\n",
            "  <tf.Variable 'Embeddings/sharded_0:0' shape=(13334, 320) dtype=float32>\n",
            "It is possible that this is intended behavior, but it is more likely\n",
            "an omission. This is a strong indication that this layer should be\n",
            "formulated as a subclassed Layer rather than a Lambda layer.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0FzhNmTCJKNM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e074256-783e-41a5-988c-f8aefb1873d1"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 1)]               0         \n",
            "                                                                 \n",
            " lambda (Lambda)             (None, 512)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 6)                 1542      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 132,870\n",
            "Trainable params: 132,870\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QWQsOxK7WhQh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "679dbc3a-9474-4231-a861-1210a5ec223e"
      },
      "source": [
        "#Convert train text to  an array and labels as get_dummies\n",
        "xtrain = df_train['text'].tolist()\n",
        "xtrain = np.array(xtrain).reshape(-1,1)\n",
        "print(xtrain.shape)\n",
        "xtest = df_test['text'].tolist()\n",
        "xtest = np.array(xtest).reshape(-1,1)\n",
        "print(xtest.shape)\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5452, 1)\n",
            "(500, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UbY6YbU02rWs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "538c8736-4c5e-40cf-e530-01faa7807c1f"
      },
      "source": [
        "categories = list(pd.get_dummies(df_train['label']).columns)\n",
        "categories = [i[2:] for i in categories]\n",
        "categories"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['BR', 'SC', 'TY', 'M', 'C', 'M']"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-CLztDgWmlM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "840952b0-cb7a-4b5f-b237-d1cc81d394d3"
      },
      "source": [
        "ytrain = np.array(pd.get_dummies(df_train['label']))\n",
        "ytest = np.array(pd.get_dummies(df_test['label']))\n",
        "print(ytrain.shape)\n",
        "print(ytest.shape)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5452, 6)\n",
            "(500, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1EjRgmhrWtqF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ae6d7f9-e095-4670-e8b8-a81e4baf2cac"
      },
      "source": [
        "model.fit(xtrain,ytrain,epochs=10,batch_size=32,validation_data=(xtest,ytest))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "171/171 [==============================] - 7s 40ms/step - loss: 0.0764 - accuracy: 0.9769 - val_loss: 0.1250 - val_accuracy: 0.9540\n",
            "Epoch 2/10\n",
            "171/171 [==============================] - 7s 39ms/step - loss: 0.0670 - accuracy: 0.9826 - val_loss: 0.1304 - val_accuracy: 0.9520\n",
            "Epoch 3/10\n",
            "171/171 [==============================] - 7s 40ms/step - loss: 0.0605 - accuracy: 0.9848 - val_loss: 0.1216 - val_accuracy: 0.9580\n",
            "Epoch 4/10\n",
            "171/171 [==============================] - 7s 39ms/step - loss: 0.0562 - accuracy: 0.9851 - val_loss: 0.1440 - val_accuracy: 0.9500\n",
            "Epoch 5/10\n",
            "171/171 [==============================] - 7s 39ms/step - loss: 0.0480 - accuracy: 0.9899 - val_loss: 0.1368 - val_accuracy: 0.9520\n",
            "Epoch 6/10\n",
            "171/171 [==============================] - 7s 40ms/step - loss: 0.0422 - accuracy: 0.9923 - val_loss: 0.1459 - val_accuracy: 0.9540\n",
            "Epoch 7/10\n",
            "171/171 [==============================] - 7s 41ms/step - loss: 0.0377 - accuracy: 0.9938 - val_loss: 0.1471 - val_accuracy: 0.9520\n",
            "Epoch 8/10\n",
            "171/171 [==============================] - 7s 39ms/step - loss: 0.0344 - accuracy: 0.9945 - val_loss: 0.1397 - val_accuracy: 0.9600\n",
            "Epoch 9/10\n",
            "171/171 [==============================] - 7s 39ms/step - loss: 0.0286 - accuracy: 0.9958 - val_loss: 0.1661 - val_accuracy: 0.9540\n",
            "Epoch 10/10\n",
            "171/171 [==============================] - 7s 40ms/step - loss: 0.0246 - accuracy: 0.9971 - val_loss: 0.1446 - val_accuracy: 0.9580\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f0e430e14d0>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WuxHBBIVMNlZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "cffd0e12-7c57-4db8-92c1-195b82d6d4bb"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model, show_shapes=True, dpi=96)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0xAbnzt6cZW"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sw3ZiaqR6gSl"
      },
      "source": [
        "## Test the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OYlCgnwJIYl"
      },
      "source": [
        " model.save_weights(\"model.h5\")"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "adGmjbgDW2iS"
      },
      "source": [
        "new_doc = [\"Who is president of United States of America?\",\"What is price for Harley Davidson?\"]\n",
        "new_doc = np.array(new_doc).reshape(-1,1)\n",
        "\n",
        "model.load_weights(\"model.h5\")\n",
        "output = model.predict(new_doc)\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-DngzWTNJ-Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43521a0b-9dc0-4c5c-8014-b4a9d918411e"
      },
      "source": [
        "output"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3.9927625e-07, 3.4952511e-06, 1.6139045e-06, 9.9998546e-01,\n",
              "        8.8773468e-06, 1.1831874e-07],\n",
              "       [7.2777939e-06, 3.2830583e-03, 2.0624211e-04, 1.5917187e-05,\n",
              "        2.1391646e-05, 9.9646616e-01]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pXyQuJGPPNvp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70f74882-8923-4d14-8316-bbd5b26e958c"
      },
      "source": [
        "output2 = [np.argmax(i) for i in output]\n",
        "output2"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 5]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ux21-7eb2l85",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd1bd783-c452-4c26-811d-10c34e1224b0"
      },
      "source": [
        "categories = list(pd.get_dummies(df_train['label']).columns)\n",
        "output2 = [categories[i] for i in output2]\n",
        "output2"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['HUM', 'NUM']"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YR-Mw9pPNBhh"
      },
      "source": [
        ""
      ],
      "execution_count": 26,
      "outputs": []
    }
  ]
}