src/attention.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "wokR3ujrNEbk"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import nltk\n",
        "import keras\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional,InputLayer\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "azL1Ja52PmXq",
        "outputId": "7434a5c7-a57f-4344-99e0-c7c1ba07a047"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading collection 'all'\n",
            "[nltk_data]    | \n",
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            "[nltk_data]    |   Unzipping help/tagsets.zip.\n",
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            "[nltk_data]    |   Unzipping corpora/unicode_samples.zip.\n",
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            "[nltk_data]    |   Unzipping taggers/universal_tagset.zip.\n",
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            "[nltk_data]    |   Unzipping corpora/verbnet.zip.\n",
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            "[nltk_data]    |   Unzipping models/wmt15_eval.zip.\n",
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            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping models/word2vec_sample.zip.\n",
            "[nltk_data]    | Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/wordnet.zip.\n",
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            "[nltk_data]    |   Unzipping corpora/wordnet2021.zip.\n",
            "[nltk_data]    | Downloading package wordnet31 to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/wordnet31.zip.\n",
            "[nltk_data]    | Downloading package wordnet_ic to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/wordnet_ic.zip.\n",
            "[nltk_data]    | Downloading package words to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/words.zip.\n",
            "[nltk_data]    | Downloading package ycoe to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/ycoe.zip.\n",
            "[nltk_data]    | \n",
            "[nltk_data]  Done downloading collection all\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "nltk.download('all')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "taWIzVhmPmU3",
        "outputId": "a51387b5-f916-4efc-ecd9-87661e9ea8af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "_QLAgSjkPmSV"
      },
      "outputs": [],
      "source": [
        "data_path = \"/content/drive/MyDrive/CS6890Project/data/training.1600000.processed.noemoticon.csv\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGj4vjMfPmPB",
        "outputId": "a760557a-4cca-4257-c61d-d58caf9353c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\"0\",\"1467810369\",\"Mon Apr 06 22:19:45 PDT 2009\",\"NO_QUERY\",\"_TheSpecialOne_\",\"@switchfoot http://twitpic.com/2y1zl - Awww, that's a bummer.  You shoulda got David Carr of Third Day to do it. ;D\"\n",
            "\n"
          ]
        }
      ],
      "source": [
        "file = open(data_path, \"r\")\n",
        "for line in file:\n",
        "    print(line)\n",
        "    break\n",
        "file.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "F4J3rpIaP0LY"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(data_path, header=None, encoding = \"ISO-8859-1\")\n",
        "data.columns = [\"target\", \"id\", \"date\", \"flag\", \"user\", \"text\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "qKBImBZaP0F-",
        "outputId": "c6459661-15c3-4c23-c5ca-07da1584339c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target          id                          date      flag  \\\n",
              "0       0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY   \n",
              "1       0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   \n",
              "2       0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY   \n",
              "3       0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
              "4       0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
              "\n",
              "              user                                               text  \n",
              "0  _TheSpecialOne_  @switchfoot http://twitpic.com/2y1zl - Awww, t...  \n",
              "1    scotthamilton  is upset that he can't update his Facebook by ...  \n",
              "2         mattycus  @Kenichan I dived many times for the ball. Man...  \n",
              "3          ElleCTF    my whole body feels itchy and like its on fire   \n",
              "4           Karoli  @nationwideclass no, it's not behaving at all....  "
            ],
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              "      <td>Mon Apr 06 22:19:45 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
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              "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n",
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              "      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>scotthamilton</td>\n",
              "      <td>is upset that he can't update his Facebook by ...</td>\n",
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              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1467810917</td>\n",
              "      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>mattycus</td>\n",
              "      <td>@Kenichan I dived many times for the ball. Man...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1467811184</td>\n",
              "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>ElleCTF</td>\n",
              "      <td>my whole body feels itchy and like its on fire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1467811193</td>\n",
              "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>Karoli</td>\n",
              "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
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              "  </tbody>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-203eafea-babb-4c52-bfba-9eccad41cd50');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "y_c9UuL6P4bl"
      },
      "outputs": [],
      "source": [
        "X=np.array(data['text'])\n",
        "Y=np.array(data['target'])\n",
        "Y[Y==4]=1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2uX0tu45P4Yy",
        "outputId": "07679672-cb95-4175-9e16-d2339625fac8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "@islandiva147 I sent u a tweet yesterday but I don't know why it didn't work  I guess you're sleeping right now I am working soon noon !!!\n"
          ]
        }
      ],
      "source": [
        "index = 3000 \n",
        "print(X[index])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H996134KfcUr"
      },
      "source": [
        "#Data pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "QDtNdN84P4WG"
      },
      "outputs": [],
      "source": [
        "def tokenize(X):\n",
        "  \"\"\"\n",
        "  Tokenize the data using nltk\n",
        "  \"\"\"\n",
        "  treebank = nltk.tokenize.TreebankWordTokenizer()\n",
        "  X_tokenized=[treebank.tokenize(sentence) for sentence in X]  \n",
        "  return X_tokenized\n",
        "\n",
        "\n",
        "def remove_stopwords(X):\n",
        "  \"\"\"\n",
        "  Remove Stopwords using nltk\n",
        "  \"\"\"\n",
        "  stopwords=nltk.corpus.stopwords.words('english') + ['@']\n",
        "  X_no_stopwords = []\n",
        "  for sentence in X:\n",
        "    temp = [word for word in sentence if not word in stopwords]\n",
        "    X_no_stopwords.append(temp) \n",
        "  return X_no_stopwords\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def stem(X,type='porter'):\n",
        "  \"\"\"\n",
        "  Perform Stemming using nltk\n",
        "  type = 'Porter','Snowball','Lancaster'\n",
        "  \"\"\"\n",
        "  if type == 'porter':\n",
        "    stemmer= nltk.stem.PorterStemmer()\n",
        "  elif type == 'snowball':\n",
        "    stemmer = nltk.stem.SnowballStemmer()\n",
        "  elif type == 'lancaster':\n",
        "    stemmer = nltk.stem.LancasterStemmer()    \n",
        "  X_stemmed = []\n",
        "  for sentence in X:\n",
        "    temp = [stemmer.stem(word) for word in sentence]\n",
        "    X_stemmed.append(temp)\n",
        "  return X_stemmed \n"
      ],
      "metadata": {
        "id": "Mq50Jm9ZBUBH"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_wordnet_pos(treebank_tag):\n",
        "        \"\"\"\n",
        "        return WORDNET POS compliance to WORDENT lemmatization (a,n,r,v) \n",
        "        \"\"\"\n",
        "        if treebank_tag.startswith('J'):\n",
        "            return 'a'\n",
        "        elif treebank_tag.startswith('V'):\n",
        "            return 'v'\n",
        "        elif treebank_tag.startswith('N'):\n",
        "            return 'n'\n",
        "        elif treebank_tag.startswith('R'):\n",
        "            return 'r'\n",
        "        else:\n",
        "            return 'n'\n",
        "\n",
        "def lemmatize(X):\n",
        "  \"\"\"\n",
        "  Lemmatize words using corresponding POS tag\n",
        "  \"\"\"\n",
        "  lemmatizer = nltk.stem.WordNetLemmatizer()\n",
        "  X_pos = []\n",
        "  X_lemmatized = []\n",
        "\n",
        "  for sentence in X :\n",
        "    temp = nltk.pos_tag(sentence)\n",
        "    X_pos.append(temp)  \n",
        "\n",
        "  for sentence in X_pos :\n",
        "    temp = [ lemmatizer.lemmatize(word[0],pos=get_wordnet_pos(word[1])) for word in sentence]\n",
        "    X_lemmatized.append(temp)  \n",
        "  return X_lemmatized    "
      ],
      "metadata": {
        "id": "7ciRBxtpBZ-o"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "YZ6RsNU9P4Tf"
      },
      "outputs": [],
      "source": [
        "X_tokenized = tokenize(X)\n",
        "X_no_stopwords = remove_stopwords(X_tokenized)\n",
        "X_lemmatized = lemmatize(X_no_stopwords)\n",
        "X_clean = []\n",
        "for sentence in X_lemmatized:\n",
        "  temp = \" \".join(sentence)\n",
        "  X_clean.append(temp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "4uPC5dTQQLvy"
      },
      "outputs": [],
      "source": [
        "max_vocab=50000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "lhOyYFY_QLsi"
      },
      "outputs": [],
      "source": [
        "tokenizer = Tokenizer(num_words=max_vocab,filters='@')\n",
        "tokenizer.fit_on_texts(X_clean)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "qjpMDZmOQUQH"
      },
      "outputs": [],
      "source": [
        "# Vectorize\n",
        "X_clean_vectorized=tokenizer.texts_to_sequences(X_clean)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "aYCzFo81ZOMO"
      },
      "outputs": [],
      "source": [
        "max_length=20"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y0OycdBRZOJc",
        "outputId": "ff043331-03f9-494b-8c86-d96370a00343"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1600000, 20)\n"
          ]
        }
      ],
      "source": [
        "X_clean_pad = keras.preprocessing.sequence.pad_sequences(X_clean_vectorized,max_length,padding='post',truncating='post')\n",
        "print(X_clean_pad.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7L41AmYbZOBc",
        "outputId": "c2d8b413-1001-4340-c079-4e7173c8d57b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Original text:\n",
            "@islandiva147 I sent u a tweet yesterday but I don't know why it didn't work  I guess you're sleeping right now I am working soon noon !!!\n",
            "\n",
            "Tokenized :\n",
            "['@', 'islandiva147', 'I', 'sent', 'u', 'a', 'tweet', 'yesterday', 'but', 'I', 'do', \"n't\", 'know', 'why', 'it', 'did', \"n't\", 'work', 'I', 'guess', 'you', \"'re\", 'sleeping', 'right', 'now', 'I', 'am', 'working', 'soon', 'noon', '!', '!', '!']\n",
            "\n",
            "Stopwords removed :\n",
            "['islandiva147', 'I', 'sent', 'u', 'tweet', 'yesterday', 'I', \"n't\", 'know', \"n't\", 'work', 'I', 'guess', \"'re\", 'sleeping', 'right', 'I', 'working', 'soon', 'noon', '!', '!', '!']\n",
            "\n",
            "POS tagged :\n",
            "[('islandiva147', 'NN'), ('I', 'PRP'), ('sent', 'VBD'), ('u', 'JJ'), ('tweet', 'NN'), ('yesterday', 'NN'), ('I', 'PRP'), (\"n't\", 'RB'), ('know', 'VBP'), (\"n't\", 'RB'), ('work', 'VB'), ('I', 'PRP'), ('guess', 'JJ'), (\"'re\", 'VBP'), ('sleeping', 'VBG'), ('right', 'RB'), ('I', 'PRP'), ('working', 'VBG'), ('soon', 'RB'), ('noon', 'RB'), ('!', '.'), ('!', '.'), ('!', '.')]\n",
            "\n",
            "Lemmatized :\n",
            "['islandiva147', 'I', 'send', 'u', 'tweet', 'yesterday', 'I', \"n't\", 'know', \"n't\", 'work', 'I', 'guess', \"'re\", 'sleep', 'right', 'I', 'work', 'soon', 'noon', '!', '!', '!']\n",
            "\n",
            "Clean :\n",
            "islandiva147 I send u tweet yesterday I n't know n't work I guess 're sleep right I work soon noon ! ! !\n",
            "\n",
            "Vectorized :\n",
            "[2, 250, 23, 108, 260, 2, 8, 28, 8, 18, 2, 188, 67, 75, 80, 2, 18, 137, 2682, 1, 1, 1]\n",
            "\n",
            "Padded :\n",
            "[   2  250   23  108  260    2    8   28    8   18    2  188   67   75\n",
            "   80    2   18  137 2682    1]\n"
          ]
        }
      ],
      "source": [
        "index  = 3000  \n",
        "\n",
        "print('\\nOriginal text:')\n",
        "print(X[index])\n",
        "print('\\nTokenized :')\n",
        "print(X_tokenized[index])\n",
        "print('\\nStopwords removed :')\n",
        "print(X_no_stopwords[index])\n",
        "print('\\nPOS tagged :')\n",
        "print(nltk.pos_tag(X_no_stopwords[index]))\n",
        "print('\\nLemmatized :')\n",
        "print(X_lemmatized[index])\n",
        "print('\\nClean :')\n",
        "print(X_clean[index])\n",
        "print('\\nVectorized :')\n",
        "print(X_clean_vectorized[index])\n",
        "print('\\nPadded :')\n",
        "print(X_clean_pad[index])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#WordEmbeddings"
      ],
      "metadata": {
        "id": "FQKFsvKBCagM"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQy-VPWkgDry"
      },
      "source": [
        "**Using pre-trained Word Embeddings (GloVe)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z25E3V5RZiyj"
      },
      "outputs": [],
      "source": [
        "embedding_path = \"/content/drive/MyDrive/CS6890Project/data/glove.6B.100d.txt\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QDux-Z4GZb0w"
      },
      "outputs": [],
      "source": [
        "emb = dict()\n",
        "f = open(embedding_path)\n",
        "for line in f:\n",
        "\tvalues = line.split()\n",
        "\tword = values[0]\n",
        "\tcf = np.asarray(values[1:], dtype='float32')\n",
        "\temb[word] = cf\n",
        "f.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LA1JWIRZZbxe"
      },
      "outputs": [],
      "source": [
        "embedding_matrix = np.zeros((max_vocab, 100))\n",
        "for i in range(1,max_vocab):\n",
        "\tvector = emb.get(tokenizer.index_word[i])\n",
        "\tif vector is not None:\n",
        "\t\tembedding_matrix[i] = vector"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jbZIZ2FkZ5Hs",
        "outputId": "d40f07e0-dc04-4b46-b1d0-cc42a7edc9bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1120000, 20)\n",
            "(480000, 20)\n",
            "(1120000, 1)\n",
            "(480000, 1)\n"
          ]
        }
      ],
      "source": [
        "np.random.seed(42)\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split (X_clean_pad,Y.reshape(Y.shape[0],1),test_size=0.30)\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(Y_train.shape)\n",
        "print(Y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "OgY-3sTYGxN6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Attention Layer"
      ],
      "metadata": {
        "id": "qEWdsJLRNdpV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " import numpy as np\n",
        " from keras.preprocessing import sequence\n",
        " from keras.models import Sequential\n",
        " from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional\n",
        " from keras.datasets import imdb "
      ],
      "metadata": {
        "id": "kJi5oHtvohzY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import *\n",
        "from keras.models import *\n",
        "from keras import backend as K"
      ],
      "metadata": {
        "id": "DJYJ8bUSDv3S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_unique_words = 10000\n",
        "maxlen = 200"
      ],
      "metadata": {
        "id": "BHkCkiX5EXHB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Layer\n",
        "from tensorflow.keras import backend as K\n",
        "\n",
        "class Attention(Layer):\n",
        "    \n",
        "    def __init__(self, return_sequences=True):\n",
        "        self.return_sequences = return_sequences\n",
        "        super(Attention,self).__init__()\n",
        "        \n",
        "    def build(self, input_shape):\n",
        "        \n",
        "        self.W=self.add_weight(name=\"att_weight\", shape=(input_shape[-1],1),\n",
        "                               initializer=\"normal\")\n",
        "        self.b=self.add_weight(name=\"att_bias\", shape=(input_shape[1],1),\n",
        "                               initializer=\"zeros\")\n",
        "        self.b=self.add_weight(name=\"att_bias\", shape=(input_shape[1],1))\n",
        "        self.b=self.add_weight(name=\"att_bias\", shape=(input_shape[1],1))\n",
        "\n",
        "        super(Attention,self).build(input_shape)\n",
        "        \n",
        "    def call(self, x):\n",
        "        \n",
        "        e = K.tanh(K.dot(x,self.W)+self.b)\n",
        "        a = K.softmax(e, axis=1)\n",
        "        output = x*a\n",
        "        \n",
        "        if self.return_sequences:\n",
        "            return output\n",
        "        \n",
        "        return K.sum(output, axis=1)"
      ],
      "metadata": {
        "id": "jEcz3r1wD1TK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_len = 20\n",
        "max_words = 50000\n",
        "emb_dim = 128"
      ],
      "metadata": {
        "id": "nQvg5g19IHEe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "att_model = keras.models.Sequential(name='att_model')\n",
        "\n",
        "att_model.add(InputLayer(input_shape=(20,),name = 'Integer_Encoding'))\n",
        "att_model.add(Embedding(max_vocab, 100,input_length=20,mask_zero=True,name='100D_Encoding',trainable=False))\n",
        "att_model.add(Bidirectional(LSTM(64,name='LSTM'),name='Bidirectional_RNN'))\n",
        "att_model.add(Attention(return_sequences=True))\n",
        "att_model.add(Dropout(0.5,name='Regularizer'))\n",
        "att_model.add(Dense(1, activation='sigmoid',name='Sigmoid_Classifier'))\n"
      ],
      "metadata": {
        "id": "HHdHkuRqN8oe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(att_model,to_file='model.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "XCRE38i5lI3q",
        "outputId": "093b242f-d201-4f14-edd8-85c1c86b70ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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9HRoaysAiaiZeHiRqpokTJ0JRlEbbaLVaTJs2rXUKImrDeHmQyAEGDx6MAwcOoKampt75iqLg+PHjCA4Obt3CiNoYnmkROcDUqVOh0dR/OCmKghtuuIGBReQADC0iB4iNjW3wLEuj0WDq1KmtXBFR28TQInIAf39/DB8+HC4uLvXOj4qKauWKiNomhhaRg0yZMqXONI1Gg5EjR8LPz88JFRG1PQwtIgeJiYmp932t+sKMiJqGoUXkIF5eXrjjjjvg6vp/H390cXHB3Xff7cSqiNoWhhaRA02ePBnV1dUAAFdXV0RERMBkMjm5KqK2g6FF5EAREREwGAwAgOrqakyaNMnJFRG1LQwtIgfS6/UYP348AMBoNGLMmDFOroiobeF3DzpIdnY2vvnmG2eXQVeBwMBAAEBYWBh27Njh5GroahAYGIhhw4Y5u4w2gV/j5CCpqamIjY11dhlEdBWKjo5GWlqas8toE3im5WD8H4AAYOHChUhMTLS6k7CpYmJiAIAveipVO37kGHxPi6gFOCqwiMgaQ4uoBTCwiFoGQ4uIiFSDoUVERKrB0CIiItVgaBERkWowtIiISDUYWkREpBoMLSIiUg2GFhERqQZDi4iIVIOhRUREqsHQIiIi1WBoERGRajC0rgI1NTVITk5GeHh4g22+/vpr3HTTTTAajejUqRPmzZuH8vJyu9tt2bIF3bp1g6IoVg83Nzd07NgRI0aMwLJly5Cfn9+kbWmo/0sfwcHBTeq7pTzwwAPw9PSEoig4cOCAZfoHH3wAk8mEnTt3OrE6+7XmGISFhcHFxQV/+MMfHNLfpRoaF1vbqXX8qHEMLSc7cuQI/vjHP+Kxxx6D2Wyut01mZiZGjx6NUaNG4dy5c9i6dSv+/ve/Y8aMGXa3i4qKwvHjxxESEgKTyQQRQU1NDXJzc5GamoquXbti3rx56Nu3L/bt22f39tTXv4igqqoKZrMZZ8+ehdFotLvflvTWW2/hzTffrDNdrb+N1ppjsHfvXowcOdIhfV2uoXGxtZ1ax48ax9Byoh9//BFPPvkkZsyY0eh/qosXL4a/vz+effZZuLu7Y9iwYZg3bx7+8Y9/4NChQ3a3u5yiKPD29saIESOwfv16pKam4uzZsxg7diwKCwsdsq0uLi4wGAzo2LEjevbs6ZA+W1rt9o8bN87ZpThES46BoigO7c8R2tr40e8YWk40cOBAbNmyBZMmTYJOp6u3TVVVFd5//33ccsstVi8MY8aMgYhg+/btdrWzRXR0NKZNm4bc3Fy88cYbTdy6hm3bts3hfTZXa7zoigjS0tKwdu3aFl/XlTh6DLRarUP7q2XruFxr43ctY2hd5Y4fP46SkhIEBQVZTQ8JCQEAZGRk2NXOVtOmTQMAfPjhh00p22Zr1qyBu7s7jEYjtm/fjjFjxsDLywsBAQHYuHFjnfYbNmzAkCFDoNfr4e7ujuDgYCxevBjA7y8qy5cvR+/evaHT6eDj44N77rmnzlmmiGDZsmXo1asXdDodTCYT5s6da9Xm66+/RlBQEBRFwapVq+yutbq6GkuWLEGvXr1gMBjQoUMHdO3aFUuWLMGECRMc+RQ224oVK+Du7g6NRoPBgwfDz88PWq0W7u7uGDRoEIYPH47AwEDo9Xp4e3vjiSeeqNPH0aNHERoaCnd3dxgMBgwfPhxff/21VZvq6mokJSUhKCgIBoMBAwYMQEpKimW+LeNia7trafyuOUIOkZKSIs15Om+88UYZOHBgnelffvmlAJBly5bVmWcwGGTUqFF2tasVEhIiJpOpwXqKiooEgAQGBtq7KQ32P2vWLDl48GCdtk8//bQAkE8//VQKCwslNzdXhg8fLu7u7lJRUWFpl5ycLADkhRdekLy8PLlw4YL87W9/k0mTJomISFJSkri5ucmGDRukoKBAMjIyZNCgQdKhQwfJycmxWp+iKPLKK69Ifn6+mM1mWb16tQCQH374wdLu1KlTAkBWrlxpd63PP/+8uLi4yPbt28VsNsv+/fvFz89PRowYYfdzGR0dLdHR0XYvZ88YPPPMMwJA0tPTpbS0VM6fPy933HGHAJD3339fzp07J6WlpZKQkCAA5MCBA5ZlR40aJd26dZNffvlFKisr5X//+5/ceOONotfr5fDhw5Z2jz/+uOh0Otm8ebPk5+fL/PnzRaPRyN69e0XE9nG5VsaP6sczratc7Z1/Li4udeZptVqUlZXZ1c5WtXdjFRcX21uyRWFhodUda6+++mqj7cPDw+Hl5QVfX1/ExcWhtLQUJ0+eBABUVlbi2WefxciRI/Hkk0+iXbt28PHxwf3334+wsDCUlZVh+fLlGD9+PCZPngyTyYT+/fvjjTfewPnz5y2XdMrKypCcnIw//elPeOyxx+Dt7Q2DwYB27drZtW2N1Qr8fvlt8ODBiIiIgMFgwKBBg3D33Xdj9+7dqKiosPOZbDp7x6BPnz4wGo1o3749Jk6cCAAICgpChw4dYDQaMXnyZACoc/bq6emJ4OBguLq6om/fvnjzzTdx8eJFy/N+8eJFrFmzBpGRkYiKioK3tzcSExOh1Wqxfv16m8flWhs/qouhdZXT6/UAfn/P6nIVFRUwGAx2tbNVaWkpRAReXl72lmxx6Z1rIoJZs2bZvKybmxuA38MK+P3yZkFBAW6//Xardi4uLpg1axYyMzNRUlKCIUOGWM0PCwuDm5sb0tPTAfx+GctsNmPUqFFN3q4r1Qr8/iItl929Vl1dDa1WW+8/Fi3FEWNw6T5V+97Vpdtan/79+8NkMlkuS2dlZcFsNqNfv36WNgaDAf7+/jh06JDN43KtjR/VxdC6yvn7+wMAioqKrKabzWZcvHgRnTp1squdrQ4fPgwACA0NbVLd9VmxYoXVi5Y9arfL29u73vkFBQUAAA8PjzrzvL29LWeM2dnZAABfX98m1WGrO++8E/v378f27dtRVlaGffv2Ydu2bbjrrruc+qLXnDGwl1artQRBaWkpACAxMdHqzO/EiRMwm802j8u1Pn7E0Lrqde3aFZ6enjhx4oTV9KNHjwIABgwYYFc7W3300UcAfr/78Gpw3XXXAQDOnz9f7/zaMKvvcmZBQQECAgIA/N8ZaX0fzHakhQsX4tZbb8W0adPg5eWF8ePHY8KECTZ97qgtqKqqwoULFyw3BtWGTHJystWZn4hgz549No8Lx48YWlc5V1dX3Hnnndi9ezdqamos0z/88EMoioKIiAi72tkiJycHycnJCAgIwH333ee4jfn/zpw5g+nTp9u1THBwMNq1a4ddu3bVO79fv37w8PCo84Ho9PR0VFRUYPDgwZZ2Go0GX375ZdOKt1FmZiaOHTuGc+fOobKyEidPnsSaNWvg4+PTouu1VVPGwB6ff/45ampqMGjQIACw3H3Y0Ddb2DouHD9iaKnAggULcPbsWTzzzDMoLS3Fnj17sGzZMkybNg29evWyu10tEUFJSQlqamogIjh37hxSUlJw0003wcXFBdu2bWvWe1r1ra+srAxbtmyxu1+dTof58+dj9+7dSEhIwG+//YaamhoUFxfjp59+gl6vx5w5c7B161a88847KCoqwsGDBzFjxgx06tQJ8fHxAH7/jz8qKgqbN2/GunXrUFRUhIyMDId/9uaRRx5BUFAQSkpKHNpvczVnDBpTUVGBwsJCVFVV4fvvv0dCQgK6dOli+eiEXq/H9OnTsXHjRqxZswZFRUWorq5GdnY2zpw5Y/O4XOvjR+At747SlFve9+zZIzfddJN06tRJAAgA8ff3l/DwcPnyyy+t2n755Zdyww03iE6nk06dOsncuXPl4sWLdfq8UrsdO3bIgAEDxGg0ipubm2g0GgEgiqKIt7e33HDDDbJo0SLJy8tr0vOwdetWCQkJsWxPQ4/ExEQREVm9erUYjUYBID169JBjx47J2rVrxcvLSwBIly5drG6bXrVqlfTv31/0er3o9Xq5/vrrZfXq1SIiUlNTI8uWLZMePXqIVqsVHx8fiYyMlKysLKsai4uL5YEHHpD27duLh4eH3HzzzZKUlCQAJCAgQH788UdZuXKl+Pv7CwAxGo0SERFhV62fffaZtG/f3mqbtVqt9O7dW7Zs2WLXc2rvLdP2jsGKFSss2xUcHCxfffWVvPjii2IymQSA+Pn5yb/+9S/ZtGmT+Pn5CQDx8fGRjRs3iojI+vXrZeTIkdKxY0dxdXWV9u3by8SJE+XEiRNWdZWXl8u8efMkKChIXF1dxdfXV6KioiQzM9PmcbkWxo8ap4jwC7ocITU1FbGxsfy+MwLw+wdZjxw5guTkZMu0iooKPPnkk1izZg3y8/NtvqMzJiYGAJCWltYitVJdHL+rl6uzCyBqa3JycpCQkFDn/Rs3NzcEBQWhsrISlZWVdn8MgVoHx+/qxve06IoOHTrU6M9c1D7i4uKcXepVwWAwQKvVYt26dTh79iwqKytx+vRpvPXWW0hKSkJcXJxD308ix+L4Xd14pkVXFBoaysuedjCZTNi1axcWLVqEnj17orS0FB4eHujbty9efPFFPPjgg84ukRrB8bu6MbSIWsDw4cPxn//8x9llUBNx/K5evDxIRESqwdAiIiLVYGgREZFqMLSIiEg1GFpERKQaDC0iIlINhhYREakGQ4uIiFSDoUVERKrB0CIiItVgaBERkWowtIiISDUYWkREpBr8lncHS01NdXYJ1MZkZ2cD4L6lVtnZ2QgICHB2GW0GQ8vBYmNjnV0CtVHct9QrOjra2SW0GYrw1/2IHE5RFKSkpGDChAnOLoWoTeF7WkREpBoMLSIiUg2GFhERqQZDi4iIVIOhRUREqsHQIiIi1WBoERGRajC0iIhINRhaRESkGgwtIiJSDYYWERGpBkOLiIhUg6FFRESqwdAiIiLVYGgREZFqMLSIiEg1GFpERKQaDC0iIlINhhYREakGQ4uIiFSDoUVERKrB0CIiItVgaBERkWowtIiISDUYWkREpBoMLSIiUg2GFhERqQZDi4iIVIOhRUREqsHQIiIi1WBoERGRajC0iIhINRhaRESkGq7OLoBI7dauXYv8/Pw607dv345ffvnFatq0adPg5+fXWqURtTmKiIiziyBSs/j4eKxduxY6nc4yTUSgKIrl76qqKphMJuTk5ECr1TqjTKI2gZcHiZpp4sSJAIDy8nLLo6KiwupvjUaDiRMnMrCImolnWkTNVFNTg06dOiE3N7fRdl9//TVuuummVqqKqG3imRZRM2k0GkyePBlubm4NtunUqRPCw8NbsSqitomhReQAEydOREVFRb3ztFotpk6davUeFxE1DS8PEjlIt27d6twtWOvAgQMYOHBgK1dE1PbwTIvIQaZOnVrvjRbdunVjYBE5CEOLyEEmT56MyspKq2larRbTp093UkVEbQ8vDxI50IABA/C///0Plx5Whw8fRo8ePZxYFVHbwTMtIgeaOnUqXFxcAACKouD6669nYBE5EEOLyIHuvfdeVFdXAwBcXFzw5z//2ckVEbUtDC0iB7ruuusQHh4ORVFQU1ODmJgYZ5dE1KYwtIgcbMqUKRAR/PGPf8R1113n7HKI2ha5TEpKigDggw8++OCDD6c+oqOjL48oafCnSVJSUhqaRURX8MorryA+Ph4eHh4tto7k5GQAwKOPPtpi6yByltr9+3INhtaECRNarBiiti48PBwBAQEtuo60tDQAPFapbardvy/H97SIWkBLBxbRtYqhRUREqsHQIiIi1WBoERGRajC0iIhINRhaRESkGgwtIiJSDYYWERGpBkOLiIhUg6FFRESqwdAiIiLVYGgREZFqMLSIiEg1GFpERKQazQ6tLVu2oFu3blAUxerh6uqKDh064E9/+hO2bt1aZ7kPPvgAJpMJO3fubLDvBx54AJ6enlAUBQcOHLBr2Zbk7PW//PLL6NixIxRFwRtvvGHXsg2Nl16vR9euXXHffffhl19+aXSZKVOm1Ol39OjR8PT0hIuLC/r27Yvvv/++yctd7pNPPsFTTz1Vpz9/f39MngjP2N4AACAASURBVDz5itv8448/Ii4uDl27doVOp0OHDh0wcOBAPPfcc5Y2cXFxdZ6Thh7vvfdenVoWLFjQaA3Lly+HoijQaDQIDQ3F7t27sWPHDixduhTV1dVX3AZH4LHa+mw5Vrl/26mhXy62V0hIiJhMJsvfFy5ckE8++URCQ0MFgGzatMmq/XvvvSdeXl6yY8eORvvduHGjAJAffvjB7mVbirPXLyJy5MgRASCvv/56k5a/dLyqq6vl7Nmz8vbbb4vRaJSOHTvK+fPn612mffv2AkDee++9OvM//PBDufvuux22nIhIUlKSjBs3ToqKiuqt/UoyMjLEaDTKrFmz5JdffpGysjLJysqSJ554QkaNGmVpFxsbK7t27ZKCggKprKyUM2fOCACJiIiQiooKKS0tldzcXPnLX/4iO3futKoFgPj7+0tFRUW9NVRVVUmXLl0EgNU6RURWrFght9xyi+Tn59u0PZeKjo6u95ddr4THautq7Fjl/t2whvbvFrs86OPjg1GjRuHVV18FAKSmplrNHzt2LAoLCzFu3Di7+27OsvYqKytDeHi409bfGjQaDTp27IgpU6bgkUceQW5uLj755JN627722mvQaDSIj49HYWGhzetoynIvvvgiNm3ahNTUVHh6etq8rku9/PLL8Pb2xooVKxAcHAy9Xo+ePXti8eLFMBgMlnaKouCmm26CyWSCq6ur1XStVguj0QhfX18MHjy4zjoGDx6MnJwcbNu2rd4atmzZgs6dO9c7b9asWRg4cCDuvPNOVFVVNWkbm4vHqnNw/26aFn9PKzg4GABQUFDQpOUVRXFgNfZbt24dcnNznVpDa+revTsAICcnp9754eHhmD17Nn777Tc8/vjjNvdr73JHjx7FggUL8Oyzz0Kv19u8nsvl5eWhsLAQFy5csJru5uZmdclo48aNMBqNV+wvPj4ed911l9W0mTNnAgBef/31epdZvnw55syZ02CfCxcuxIEDB7BixYorrr8l8VhtPdy/m67FQysjIwMAcMstt1imff311wgKCoKiKFi1apVluohg2bJl6NWrF3Q6HUwmE+bOnWvVX33LvvTSSzAajfD09ERubi7mzJmDzp07IysrC9XV1UhKSkJQUBAMBgMGDBiAlJQUqz43bNiAIUOGQK/Xw93dHcHBwVi8eDFmz56NOXPm4NixY1AUBd27d2+09uXLl6N3797Q6XTw8fHBPffcg0OHDlnarFmzBu7u7jAajdi+fTvGjBkDLy8vBAQEYOPGjVY1ffXVV+jTpw9MJhP0ej369++Pjz/+uJmjcWVHjhwBAAwcOLDBNs899xx69uyJt956q8EzsuYu99prr0FEEBERYXP/9QkLC0NpaSluvfVW/Pe//21WXw259dZb0bt3b3z++efIysqymvff//4XZrMZo0ePbnB5Hx8f3HLLLVixYgVEpEVqtAWP1dY7Vrl/N8Pl1wsd9Z6W2WyWDz/8ULp06SKjR4+WkpISq/anTp0SALJy5UrLtKeffloURZFXXnlF8vPzxWw2y+rVq+tcJ29oWQAya9YsWblypYwfP15+/vlnefzxx0Wn08nmzZslPz9f5s+fLxqNRvbu3SsiIsnJyQJAXnjhBcnLy5MLFy7I3/72N5k0aZKIiERFRUlISMgVa09KShI3NzfZsGGDFBQUSEZGhgwaNEg6dOggOTk5der89NNPpbCwUHJzc2X48OHi7u5udc04LS1NFi5cKBcuXJC8vDwZOnSotG/f3jLfke9piYjk5+fLP/7xDzEajTJ27NgGl/nll19EROSbb74RjUYjwcHBlrFt7D0te5fr1q2b9OnTx6baG2M2m2XIkCECQABInz59ZOnSpZKXl9focrXX/Bt6r+3ybXv11VcFgMyePdtqfmRkpKxfv16Ki4vrveZf66mnnqqzn1+Jo97T4rHa+scq9+8ra2j/dmho1T5xlz769+8v//znP6W8vNyq/eU7k9lsFqPRKLfddptVu/re3G3sQCgrK7NMKysrE6PRKHFxcZZpZrNZdDqdzJw5UyoqKsTb21tGjhxptc6qqipZsWKFiNh2IJjNZvHw8LBaj4jId999JwBk0aJFjdZZe7AfPXq0oadXlixZIgAkNzdXRBwTWpePlaIo8txzzzX4huul4SMiMmfOHAEgjzzyiIjYFlq2LFdSUiKKosi4ceMarMPWg1pEpKKiQl599VXLjQYApGPHjvLFF180uIy9B3VBQYG4u7uLj4+PmM1mERE5duyYBAQESHl5+RUP6r///e8CQN5++22bt6s5ocVj1XnHKvdv27TKjRgmkwnyexCisrIS2dnZePTRR5GQkIABAwbg/PnzDS579OhRmM1mjBo1ymH1ZGVlwWw2o1+/fpZpBoMB/v7+OHToEDIyMlBQUIDbb7/dajkXFxfMmjXL5vVkZmaipKQEQ4YMsZoeFhYGNzc3pKenN7q8m5sbAKCysrLBNlqtFgAcevvopeM1d+5ciAhMJpNlXVfy3HPPoVevXli9ejW+/vprm9d7peVyc3MhIjZdg7eFVqtFQkICfv75Z3z77be45557kJubi5iYGOTn5ztkHSaTCffeey/y8/OxadMmAEBycjJmzpxpGd/G1G7r2bNnHVLPlfBYdd6xyv27eVrsPS1XV1d07twZ06dPx8svv4ysrCy88MILDbbPzs4GAPj6+jqshtLSUgBAYmKi1ecQTpw4AbPZjKKiIgCAt7d3s9ZT+8a1h4dHnXne3t4oLi62u8/3338fI0aMgK+vL3Q6HZ544olm1XglCxYsgL+/P+bPn49Tp07ZtIxer8f69euhKAruu+8+lJWVOWS5ixcvAgB0Op19G2GDG2+8Ef/+978xY8YMnDt3Dp9//rnD+q59w/qNN95AQUEB0tLS8NBDD9m0bO2dXrXb3pp4rMLSd2scq9y/m6dVvhGjf//+AICffvqpwTa1d9CUl5c7bL21B1VycrLlv8rax549e3DdddcBQKP/Vdqi9kCqb4cvKChAQECAXf2dPHkSkZGR8Pf3R3p6OgoLC7F06dJm1Xglnp6eePHFF1FcXGzZOW0xbNgwPPbYYzhy5AgWL17skOVqd/CmnFXu3r0bycnJlr+joqLqvdW29oPOZrPZ7nU05A9/+AOGDh2K7777DvHx8YiJiYGPj49Ny1ZUVACA1W3KzsBjteWPVe7fzdMqobV//34AQK9evRps069fP2g0Gnz55ZcOW29gYCD0er3VJ/QvFRwcjHbt2mHXrl3NWk+/fv3g4eGBffv2WU1PT09HRUVFvZ99aMzBgwdRWVmJmTNnolu3btDr9a1yO/HUqVNx44034r333qvzWZ3GLF68GKGhofjhhx/sWl9Dy9V+g4A9nwOrtX//fri7u1v+Li8vr/cFuPYuqAEDBti9jsbUBv7mzZvx6KOP2rxc7bb6+fk5tB578Vht+WOV+3fzODy0ysrKUFNTAxHB6dOnsX79eiQmJqJDhw6NbqSvry+ioqKwefNmrFu3DkVFRcjIyMDatWubXIter8f06dOxceNGrFmzBkVFRaiurkZ2djbOnDkDnU6H+fPnY/fu3UhISMBvv/2GmpoaFBcXW3aEdu3a4fTp0/j1119RXFxc77VsvV6POXPmYOvWrXjnnXdQVFSEgwcPYsaMGejUqRPi4+PtqjsoKAjA71/vcvHiRRw5cuSK19odQVEUvPbaa1AUBQkJCTZfD6+93Ofi4mLX+hpazmg0olu3bpbLULaorKzE2bNn8cUXX1gd1AAQGRmJ1NRUFBQUoLCwENu3b8eTTz6Ju+++2+EH9YQJE9ChQwdERkaiW7duNi9Xu621Zzqtgceqc45V7t/NdPmdGfbePbh169YG70bS6XTSo0cPmTlzppw8edKyzMqVK8Xf318AiNFolIiICBERKS4ulgceeEDat28vHh4ecvPNN0tSUpIAkICAAPnxxx/rXXbp0qViMBgEgAQGBsqGDRss6yovL5d58+ZJUFCQuLq6iq+vr0RFRUlmZqalzapVq6R///6i1+tFr9fL9ddfL6tXrxYRke+//166dOkiBoNBbr75ZklMTKy39pqaGlm2bJn06NFDtFqt+Pj4SGRkpGRlZVnWs3r1ajEajQJAevToIceOHZO1a9eKl5eXAJAuXbrI4cOHRURk3rx50q5dO/H29paYmBhZtWqVAJCQkBCZPXu2+Pn5CQBxd3eX8ePH2zxe//3vf6Vnz56WMbruuuvkoYcesmozbdo0ASDe3t7ywgsvWI1xhw4dLHf9XW7u3LlWdyM1dTkRkYSEBNFqtZY7lS7vr7HH1q1bLcvs2rVLYmNjJSQkRHQ6nbi5uUmvXr1k4cKFcvHixTq1FBUVyR//+Edp166dABCNRiPdu3eX559/3qpdY9v2xBNPyDfffGP5+9J9RqPRSJ8+feSrr76y6m/s2LHSuXNnqampqfc5qo+9dw/yWL16jlXu31fW4re8EznSkSNHxNXV1epFra06f/686PV6efnll+1arqm3vJPzcf++slb/7kGi5ujevTsWLVqERYsWoaSkxNnltKiFCxfiD3/4AxISEpxdCrUS7t9Nx9BqAw4dOmTTTw7ExcU5u1S7PPXUU4iJiUFcXFyT3rRWg+XLl+PAgQP44IMPbP58HLUN3L+bhqHVBoSGhta5Tbi+R+2HAtXk+eefR0JCQqOfG1Kr7du3o7y8HF988YXNtw5T28L9236KiPU3GKampiI2NtapX9xJRFcWExMDAEhLS3NyJUSO19D+zTMtIiJSDYYWERGpBkOLiIhUg6FFRESqwdAiIiLVYGgREZFqMLSIiEg1GFpERKQaDC0iIlINhhYREakGQ4uIiFSDoUVERKrB0CIiItVwbWiGoiitWQcRNRGPVWqroqOj60yr89Mk2dnZ+Oabb1qtKKK2KDY2FrNnz8awYcOcXQqRagUGBtY5huqEFhE1n6IoSElJwYQJE5xdClGbwve0iIhINRhaRESkGgwtIiJSDYYWERGpBkOLiIhUg6FFRESqwdAiIiLVYGgREZFqMLSIiEg1GFpERKQaDC0iIlINhhYREakGQ4uIiFSDoUVERKrB0CIiItVgaBERkWowtIiISDUYWkREpBoMLSIiUg2GFhERqQZDi4iIVIOhRUREqsHQIiIi1WBoERGRajC0iIhINRhaRESkGgwtIiJSDYYWERGpBkOLiIhUg6FFRESqwdAiIiLVYGgREZFquDq7ACK1O3HiBKqrq+tMP3v2LI4fP241rVOnTjAYDK1VGlGbo4iIOLsIIjUbM2YMPvrooyu2c3V1RU5ODtq3b98KVRG1Tbw8SNRMcXFxUBSl0TYajQa33XYbA4uomRhaRM00fvx4aLXaK7abMmVKK1RD1LYxtIiaydPTE3fddVejwaXVajFu3LhWrIqobWJoETnApEmTUFVVVe88V1dXREZGwsPDo5WrImp7GFpEDjB27Fi4u7vXO6+6uhqTJk1q5YqI2iaGFpED6HQ6REdHw83Nrc48Dw8PjB492glVEbU9DC0iB7n33ntRUVFhNU2r1SIuLq7eMCMi+/FzWkQOUlNTAz8/P5w/f95q+ueff44RI0Y4pyiiNoZnWkQOotFocO+991qdVfn6+mL48OFOrIqobWFoETnQxIkTLZcI3dzcMHXqVLi4uDi5KqK2g5cHiRxIRNClSxecOnUKALB3714MGTLEyVURtR080yJyIEVRMHXqVABAly5dGFhEDsZveVe5PXv2YPny5c4ugy5RVFQEAHB3d0dMTIyTq6FLDRs2DI899pizy6Bm4JmWyp06dQqbN292dhl0CS8vL5hMJgQEBNSZl52dzfFykm+//RZ79uxxdhnUTDzTaiPS0tKcXQJd4uOPP8btt99eZ3pqaipiY2M5Xk7As962gWdaRC2gvsAiouZjaBERkWowtIiISDUYWkREpBoMLSIiUg2GFhERqQZDi4iIVIOhRUREqsHQIiIi1WBoERGRajC0iIhINRhaRESkGgwtIiJSDYYWERGpBkOLrkoffPABTCYTdu7c6exSsHTpUoSGhsJgMMDd3R2hoaFYsGCB5cceW9u7774LRVEQHh7ulPXb42oaR2obGFp0VRIRZ5dg8dVXX+Evf/kLTp48ibNnz2Lx4sVYunQpoqOjnVLPu+++i5CQEOzZswdHjx51Sg22uprGkdoGhhbVUVZWVue/+PqmteT6xo4di8LCQowbN65F1mkPNzc3PPzww/D19YWHhwdiYmJwzz334D//+Q/OnDnTqrXk5eXhp59+wrPPPgsAePvttxtsy3GktoihRXWsW7cOubm5V5zWkuu7mmzduhV6vd5qWufOnQEAJSUlrVpLamoqxo4di4iICOj1emzYsKHBsxmOI7VJQqqWkpIi9g7j7t27pXfv3uLl5SU6nU769esnH330kYiIzJo1S9zc3ASAAJCQkJB6p4mIVFVVyYIFCyQwMFD0er30799fNm3aJCIiq1evFqPRKAaDQbZt2yZ33HGHeHp6SufOneXdd9+11FJf31999ZUEBgYKAFm5cqWlbU1NjbzyyisSGhoqbm5u4u3tLXfffbf8/PPPlja2rre5xo4dK97e3lJeXm7Xck0Zr0vdfPPN8tlnn4mISEREhACQL7/8sk47jmNd0dHREh0d3aRl6erB0FK5prwIpqWlycKFC+XChQuSl5cnQ4cOlfbt21vmR0VFWV7QGpv2+OOPi06nk82bN0t+fr7Mnz9fNBqN7N27V0REnn76aQEgn376qRQWFkpubq4MHz5c3N3dpaKiotG+T506VefFLikpSdzc3GTDhg1SUFAgGRkZMmjQIOnQoYPk5ORY2tm6XntVVFRIdna2rFy5UnQ6nWzYsMHuPpoTWidOnBBfX1+pqqoSEZENGzYIALn//vvrbc9xtMbQaht4efAaFB0djWeeeQY+Pj5o164dIiIikJeXh3Pnztncx8WLF7FmzRpERkYiKioK3t7eSExMhFarxfr1663ahoeHw8vLC76+voiLi0NpaSlOnjxpV81lZWVYvnw5xo8fj8mTJ8NkMqF///544403cP78eaxdu7bOMo5Y76UCAwMREBCAhQsX4qWXXkJsbGyT+2qKd999F3fddRdcXFwAABEREdDpdEhLS0NZWVmT+rwWx5HUjaFF0Gq1AIDq6mqbl8nKyoLZbEa/fv0s0wwGA/z9/XHo0KEGl3NzcwMAVFZW2lVjZmYmSkpKMGTIEKvpYWFhcHNzQ3p6eqPLN3W9lzp16hRyc3Px7rvv4p///Ceuv/76Vn0P591338X48eMtf3t5eWH06NEoKirC9u3bm9TntTiOpG4MrWvQ+++/jxEjRsDX1xc6nQ5PPPGE3X2UlpYCABITE6EoiuVx4sQJmM1mR5eMgoICAICHh0eded7e3iguLnb4Oi+n1Wrh6+uL0aNHY9OmTcjMzMSSJUtafL0A8L///Q8HDx7EuHHjrJ7v2s8/NXYXYWOuxXEkdWNoXWNOnjyJyMhI+Pv7Iz09HYWFhVi6dKnd/fj6+gIAkpOTIb+/N2p57Nmzx9Flw9vbGwDqfVErKChAQECAw9fZmO7du8PFxQWZmZmtsr5//etfmDhxYp3n+sKFCzAYDNi1axdycnLs7vdaH0dSH4bWNebgwYOorKzEzJkz0a1bN+j1eiiKYnc/gYGB0Ov1OHDgQAtUWVe/fv3g4eGBffv2WU1PT09HRUUFBg8e3CLrzcvLw7333ltn+pEjR1BdXY3AwMAWWe+lRASbNm3Cww8/XGeej48PYmJiUF1djXfffdfuvq+VcaS2g6F1jQkKCgIAfPLJJ7h48SKOHDlS532Edu3a4fTp0/j1119RXFyMysrKOtNcXFwwffp0bNy4EWvWrEFRURGqq6uRnZ1t9wdu61vf5fR6PebMmYOtW7finXfeQVFREQ4ePIgZM2agU6dOiI+Pb/qT0gh3d3fs2rULn332GYqKilBZWYkffvgBf/7zn+Hu7o7HHnusRdZ7qW+++QZeXl646aab6p0/Y8YMAHUvEXIcqU1yyj2L5DBNuYV63rx50q5dO/H29paYmBhZtWqV5bM1J0+elO+//166dOkiBoNBbr75ZsnJyal3Wnl5ucybN0+CgoLE1dVVfH19JSoqSjIzMy2fswEgPXr0kGPHjsnatWvFy8tLAEiXLl3k8OHDIiJ1+k5MTBR/f38BIEajUSIiIkTk98/3LFu2THr06CFarVZ8fHwkMjJSsrKyLNtmz3ptFRERIV27dhUPDw/R6XQSEhIicXFxcvDgQbv6EbF/vO6//35xd3cXV1dXGThwoHz//fdW8xcvXiydOnWyfD6qc+fOsnr1ahGp+7xe6+PIW97bBkWEXw6mZqmpqYiNjeV3vKkEx8t5YmJiAABpaWlOroSag5cHiYhINRhadM05dOiQ1e3dDT3i4uKcXSoRXcbV2QUQtbbQ0FBeniNSKZ5pERGRajC0iIhINRhaRESkGgwtIiJSDYYWERGpBkOLiIhUg6FFRESqwdAiIiLVYGgREZFqMLSIiEg1GFpERKQaDC0iIlINhhYREakGQ4uIiFSDP03SRtT+Kitd3bKzswFwvJzh22+/xdChQ51dBjUTz7RULjAwENHR0c4ugy6zY8cOnD59us70gIAAjpeTDB06FMOGDXN2GdRMivDX8IgcTlEUpKSkYMKECc4uhahN4ZkWERGpBkOLiIhUg6FFRESqwdAiIiLVYGgREZFqMLSIiEg1GFpERKQaDC0iIlINhhYREakGQ4uIiFSDoUVERKrB0CIiItVgaBERkWowtIiISDUYWkREpBoMLSIiUg2GFhERqQZDi4iIVIOhRUREqsHQIiIi1WBoERGRajC0iIhINRhaRESkGgwtIiJSDYYWERGpBkOLiIhUg6FFRESqwdAiIiLVYGgREZFqMLSIiEg1GFpERKQaDC0iIlINhhYREamGIiLi7CKI1GzKlCk4cOCA1bRff/0Vvr6+cHd3t0zTarXYuXMnOnfu3NolErUZrs4ugEjtevXqhXfeeafO9JKSEqu/Q0NDGVhEzcTLg0TNNHHiRCiK0mgbrVaLadOmtU5BRG0YLw8SOcDgwYNx4MAB1NTU1DtfURQcP34cwcHBrVsYURvDMy0iB5g6dSo0mvoPJ0VRcMMNNzCwiByAoUXkALGxsQ2eZWk0GkydOrWVKyJqmxhaRA7g7++P4cOHw8XFpd75UVFRrVwRUdvE0CJykClTptSZptFoMHLkSPj5+TmhIqK2h6FF5CAxMTH1vq9VX5gRUdMwtIgcxMvLC3fccQdcXf/v448uLi64++67nVgVUdvC0CJyoMmTJ6O6uhoA4OrqioiICJhMJidXRdR2MLSIHCgiIgIGgwEAUF1djUmTJjm5IqK2haFF5EB6vR7jx48HABiNRowZM8bJFRG1LfzuwTYmNTXV2SVc8wIDAwEAYWFh2LFjh5OrofDwcAQEBDi7DHIQfo1TG3Ol78AjutakpKRgwoQJzi6DHISXB9uglJQUiAgfTnw888wzqKystKltdHQ0oqOjnV5zW3xQ28PQImoBiYmJVre+E5FjMLSIWgADi6hlMLSIiEg1GFpERKQaDC0iIlINhhYREakGQ4uIiFSDoUVERKrB0CIiItVgaBERkWowtIiISDUYWkREpBoMLSIiUg2GFhERqQZD6xq2ZcsWdOvWDYqiWD30ej26du2K++67D7/88otTa3z55ZfRsWNHKIqCN954o9n9ffDBBzCZTNi5c6cDqnOOhsbNzc0NHTt2xIgRI7Bs2TLk5+c7u1Qih2NoXcOioqJw/PhxhISEwGQyQURQXV2NkydPYtGiRUhJScHQoUORl5fntBoff/xxfPPNNw7rry38xlJ941ZTU4Pc3Fykpqaia9eumDdvHvr27Yt9+/Y5u1wih2JokRWNRoOOHTtiypQpeOSRR5Cbm4tPPvnE2WU5zNixY1FYWIhx48Y5uxSHUhQF3t7eGDFiBNavX4/U1FScPXvWsr1qV1ZWhvDwcGeXQVcBhhY1qHv37gCAnJycZvclIkhLS8PatWub3RddWXR0NKZNm4bc3FyHXFZ1tnXr1iE3N9fZZdBVgKFFDTpy5AgAYODAgVbTq6urkZSUhKCgIBgMBgwYMAApKSlW85csWYJevXrBYDCgQ4cO6Nq1K5YsWYIJEyYAABISEuDm5gZ/f3/Lcg8//DDc3d2hKArOnz/faG1fffUV+vTpA5PJBL1ej/79++Pjjz8GALz00kswGo3w9PREbm4u5syZg86dO2PdunUICgqCoihYtWoVAODo0aN13huqffznP/+54vY2tK6srKzmPPUOMW3aNADAhx9+CKDxWkUEy5cvR+/evaHT6eDj44N77rkHhw4dsvT32muvQa/Xo2PHjnjooYfQqVMn6PV6hIeHIz093WrdtvRn6z4we/ZszJkzB8eOHYOiKJZ/pugaJdSmAJCUlBS7lgkJCRGTyWT5Oz8/X/7xj3+I0WiUsWPH1mn/+OOPi06nk82bN0t+fr7Mnz9fNBqN7N27V0REnn/+eXFxcZHt27eL2WyW/fv3i5+fn4wYMcKqn0mTJomfn5/VtGXLlgkAOXfunGXakSNHBIC8/vrrlmlpaWmycOFCuXDhguTl5cnQoUOlffv2lvlPP/20AJBZs2bJypUrZfz48fLzzz/LqVOnBICsXLnS0veTTz4ppaWlIiJy5swZ8fHxkfDwcKmurrZpextal62io6MlOjra5va1Lh+3yxUVFQkACQwMvOLzkpSUJG5ubrJhwwYpKCiQjIwMGTRokHTo0EFycnIsy8fHx4u7u7v89NNPcvHiRcnMzJSwsDDx9PSUkydPWtrZ2p+t+0BUVJSEhITY/Rw15XigqxtDq41pamgBsHooiiLPPfecVFRUWLUtKysTo9EocXFxlmlms1l0Op3MnDlTRETCwsLk/dGjpAAAF05JREFUhhtusFruwQcfFI1GI+Xl5ZZpzQmtyy1ZskQASG5uroj834tzWVmZVbvLQ+tykZGRotfr5dChQzZvb0PrslVLhZaIiKIo4u3tbfm7vlrNZrN4eHhYbaOIyHfffScAZNGiRZZp8fHxdda5d+9eASDPPvus3f0xtMhevDxIAGC5C01EMHfuXIgITCYTtFqtVbusrCyYzWb069fPMs1gMMDf399y6efixYt17tKrrq6GVquFi4tLi9RfW2d1dXWT+0hNTcW///1vPPvss+jVqxcA27b3alVaWgoRgZeXV6PtMjMzUVJSgiFDhlhNDwsLg5ubW51Lf5cbMmQIjEaj5flobn9EjWFoUR0LFiyAv78/5s+fj1OnTlnNKy0tBQAkJiZavf9z4sQJmM1mAMCdd96J/fv3Y/v27SgrK8O+ffuwbds23HXXXQ4Lrffffx8jRoyAr68vdDodnnjiiWb1l5eXh7/+9a8ICwvDnDlzLNNt2d6r1eHDhwEAoaGhjbYrKCgAAHh4eNSZ5+3tjeLi4iuuS6fT4dy5cw7rj6ghDC2qw9PTEy+++CKKi4sxc+ZMq3m+vr4AgOTkZMuZWe1jz549AICFCxfi1ltvxbRp0+Dl5YXx48djwoQJePPNNx1S38mTJxEZGQl/f3+kp6ejsLAQS5cubVafs2bNQkFBAdavX28VrLZs79Xqo48+AgCMGTOm0Xbe3t4AUG+YFBQUICAgoNHlKysrrdo1tz+ixjC0qF5Tp07FjTfeiPfeew+pqamW6YGBgdDr9Thw4ECDy2ZmZuLYsWM4d+4cKisrcfLkSaxZswY+Pj5W7VxdXVFZWWl3bQcPHkRlZSVmzpyJbt26Qa/XQ1EUu/up9f777+Nf//oXFixYgL59+1qmz50716btvRrl5OQgOTkZAQEBuO+++xpt269fP3h4eNT5IHJ6ejoqKiowePDgRpf/4osvICL/r727D6qqzsMA/hzgvoLci4ig0S1A8S0pTVkGZZet3enFnVZBFHfMcHNCG6Pd1RandljHCmWVsNXYHdNpHGsIwRZ6NSurMTN3bTXBEsjNFwYJQl7kRe4VvvuHw93uApe3q4eDz2fm/uF5/Z7fOdzHc87vnIuYmJgBL2+wxwDdvBha1CNFUfDXv/4ViqIgLS3N+Uogo9GI5cuXIy8vD7m5uWhqakJHRwcqKytx8eJFAMDq1aths9nQ3Nzsdh0TJkzApUuXUFRUBIfDgdraWpw7d67P2mw2GwDgww8/xJUrV1BRUTHo+yRNTU1YuXIl7rrrLqxbtw7AtXtyx44dw4kTJ/q1vWoSETQ3N6OzsxMigtraWuTn52POnDnw9vZGUVFRn/e0jEYj1qxZgzfeeAOvvvoqmpqaUFJSglWrVmHcuHFITU11mb6zsxP19fW4evUqTp48id/97new2WzOLvYDWV5/j4HRo0ejqqoKZ8+exeXLlxl0N7Mb3/eDricMoLfU4cOHJTIy0tljcPz48bJy5UqXaVJSUgSAWK1W2bhxo4iItLe3S3p6uthsNvHx8ZGgoCBJTEyUU6dOiYjIwYMHJTAw0KU3ok6nkylTpsi+ffucy66rq5Of//znYjQaJSwsTJ544gl56qmnBIBMmDBBzp8/L9nZ2RIcHCwAxNfXVxISEkREJD09XUaPHi1Wq1WSkpJk+/btAkAiIiJk9erVYjKZnN299+zZIyIi27Ztk5CQEAEgZrNZHnroIdmyZUu3npNdnwcffLDP7c3KyupxXQMx0N6Db775pkRFRYnZbBa9Xi9eXl7OHp9Wq1Wio6Nlw4YNUldX5zKfu1o7Oztl8+bNMnHiRNHpdBIQECALFiyQsrIyl2WkpqaKTqeTW265RXx8fMTf31/mz58vZ86ccZmuv8vrzzEgIvLvf/9bbrvtNjGZTDJ37lyXbvPuDOTvgbRBERkBL2MjJ0VRkJ+f73yIVw25ubmoqKhATk6Oc5jdbse6deuQm5uL+vp6mEwm1eobbpKSkgAABQUFKlfSt5UrV6KgoEDV91EOxHD4eyDP8lG7ABpZqqurkZaW1u0ekF6vh81mg8PhgMPhYGhp2FAeKyAaKt7TIo8ymUzQ6XTYtWsXvv/+ezgcDlRVVWHnzp3IyMhAcnJyn/dYiIh6w9Aij7JYLDhw4ABKS0sRGRkJk8mEqVOn4pVXXsGmTZuwe/dutUukQXr66afxyiuvoLGxEWFhYSgsLFS7JLoJ8fIgeVxcXJzzZbM0cmRmZiIzM1PtMugmxzMtIiLSDIYWERFpBkOLiIg0g6FFRESawdAiIiLNYGgREZFmMLSIiEgzGFpERKQZDC0iItIMhhYREWkGQ4uIiDSDoUVERJrB0CIiIs3gW95HoCNHjqhdAg1AZWUlAGDv3r0qV0I0/CkiImoXQZ6jKIraJRANK/n5+Vi0aJHaZZCH8ExrhOH/QYYHRVH4ZUl0HfCeFhERaQZDi4iINIOhRUREmsHQIiIizWBoERGRZjC0iIhIMxhaRESkGQwtIiLSDIYWERFpBkOLiIg0g6FFRESawdAiIiLNYGgREZFmMLSIiEgzGFpERKQZDC0iItIMhhYREWkGQ4uIiDSDoUVERJrB0CIiIs1gaBERkWYwtIiISDMYWkREpBkMLSIi0gyGFhERaQZDi4iINIOhRUREmsHQIiIizWBoERGRZjC0iIhIMxhaRESkGQwtIiLSDIYWERFpho/aBRBp3Y4dO1BfX99teHFxMb777juXYSkpKQgODr5RpRGNOIqIiNpFEGlZamoqduzYAYPB4BwmIlAUxfnvq1evwmKxoLq6GjqdTo0yiUYEXh4kGqIlS5YAANrb250fu93u8m8vLy8sWbKEgUU0RDzTIhqizs5OjBs3DjU1NW6n++yzzzBnzpwbVBXRyMQzLaIh8vLywtKlS6HX63udZty4cYiNjb2BVRGNTAwtIg9YsmQJ7HZ7j+N0Oh2WLVvmco+LiAaHlweJPCQ8PLxbb8EuJ06cwJ133nmDKyIaeXimReQhy5Yt67GjRXh4OAOLyEMYWkQesnTpUjgcDpdhOp0Oy5cvV6kiopGHlweJPCgqKgqlpaX48Z9VeXk5Jk6cqGJVRCMHz7SIPGjZsmXw9vYGACiKghkzZjCwiDyIoUXkQb/5zW/Q0dEBAPD29sYjjzyickVEIwtDi8iDxo8fj9jYWCiKgs7OTiQlJaldEtGIwtAi8rCHH34YIoKf/vSnGD9+vNrlEI0o7IihoqSkJBQWFqpdBhENEL821cOfJlFZTEwMfv/736tdBnlYdnY2UlNT4efnp3Ypg5aTkwMAPD5/5MiRI9i6davaZdzUGFoqCw0NxaJFi9QugzwsNjYWoaGhapcxJAUFBQDA4/P/MLTUxXtaRNeB1gOLaLhiaBERkWYwtIiISDMYWkREpBkMLSIi0gyGFhERaQZDi4iINIOhRUREmsHQIiIizWBoERGRZjC0iIhIMxhaRESkGQwtIiLSDIYWERFpBkNLg7766iskJycjLCwMBoMBY8aMwZ133onnnnvOZbp3330XFosFb731lkqVutef+lasWIFRo0ZBURScOHFi0OsqKyvDE088gWnTpmHUqFHw8fGBxWJBZGQk5s2bhyNHjgAAtmzZgrFjx0JRFPz9738f9Pr6q7c2aG9vx5NPPomQkBCYzWbs379/2O/Pffv2ITw8HIqiuHz0ej3Gjh2L+Ph4bN68GfX19WqXShrG0NKYkpISxMbGIiQkBB9//DEaGxvx+eef4/7778cnn3ziMu1w/3XV/tS3c+dOvPzyy0Naz65duzB9+nScPHkSL7zwAi5cuICWlhYcP34czz77LBoaGlBSUgIAWLt2LT7//PMhrW8gemuD7Oxs7N+/H6dPn8bWrVvR3Nw87PdnYmIi/vOf/yAiIgIWiwUigs7OTtTU1GDv3r0ICwtDeno6pk2bhmPHjqldLmkUfwRSY7Zs2QKr1eryQ3SRkZF49tlnkZiY6DLtvHnz0NjYeKNL7LcbUd8XX3yB1NRU/OxnP8P7778PH5//HfLh4eEIDw+H1WpFRUXFda2jN721QVFREWbNmgWr1YrHHnvMOXw478+eKIoCq9WK+Ph4xMfHY968eVi8eDHmzZuH8vJyWCwWtUskjeGZlsbU1dWhsbERly5dchmu1+sHfdlIRFBQUIAdO3Z4okSPUxRl0PM+99xz6OjowMaNG10C68fuu+8+rF69etDruB4qKyuh0+nULsPjFi5ciJSUFNTU1NyQy6808jC0NGb27NloaWnBPffcg8OHD/c63WeffQabzQZFUbB9+3bn8I6ODmRmZmLSpEkwmUwYM2YMwsLCkJmZ6fxZ9a1bt8LX1xdeXl64++67ERwcDJ1OB19fX8ycORNxcXG49dZbYTQaYbVa8cc//tFl3SKCF154AVOmTIHBYEBAQADmz5+P06dP91mfiGDz5s2YNGkSDAYDLBYLnnrqqUG1ld1ux0cffYTAwEBER0cPahldDh06hKlTp8JiscBoNGL69Ol4//33neM//fRTREdHw2w2w9/fH9OnT0dTU5PbcT21wQcffIAJEybg4sWL2L17NxRFgZ+fn9v9mZGRAZvNBpPJhKioKOTn5wMA/vKXv8BsNmPUqFGoqanBmjVrcMstt6CsrGxIbTFUKSkpAID33nvPOczdduTm5sLX1xdmsxnFxcV44IEH4O/vj9DQUOTl5bks291+cLcO0hAh1SxcuFAWLlw4oHlaW1tl1qxZAkAAyNSpUyUrK0vq6uq6TXvhwgUBINu2bXMOe/7558Xb21uKi4ultbVVvvzySwkODpb4+HiXef/85z8LADl69Ki0tLTIDz/8IPfff78AkHfeeUdqa2ulpaVF0tLSBICcOHHCOW9GRobo9XrZs2ePNDQ0yMmTJ2XmzJkyZswYqa6udlvfM888I4qiSHZ2ttTX10tra6u89NJLAkCOHz8+oLYqLy8XABITEzOg+SoqKgSA/O1vf3MOKygokPXr18ulS5ekrq5OYmJiJDAwUEREmpubxd/fX7KysqStrU2qq6slISFBamtr3Y7rrQ1ERIKDg+WRRx5xGdbTtGvXrhWDwSCFhYVSX18vTz/9tHh5ecm//vUvEbnWngDkySeflG3btklCQoJ88803/WqHwRyfIiIRERFisVh6Hd/U1CQA5NZbbx3wdnz00UfS2NgoNTU1EhcXJ76+vmK320XE/X7ozzr6Iz8/X/i1qS62vooG+6Vgt9vlxRdflMmTJzvDa+zYsfLJJ5+4TNfTl9zs2bMlOjraZbrHHntMvLy8pL293TmsK7QuX77sHLZ7924BICUlJc5h//znPwWAvP766yJyLVT9/PwkOTnZZR1d023YsKHX+lpbW8VsNssvf/lLl3nz8vIGFVrHjh0TAPKLX/xiQPP1FFr/LzMzUwBITU2NlJaWCgB5++23u03nbpzI0EKrra1NzGazS1u3traKwWCQxx9/XET+92Xf1tbWr23/sesVWiIiiqKI1WoVkcFvR9d/Zr799lsRcd/W/VlHfzC01MfLgxqk0+mQlpaGb775Bl988QXmz5+PmpoaJCUl9dmd+MqVK916oXV0dECn08Hb29vtvHq9HgBw9epVl1oAwOFwAABOnTqF5uZmzJo1y2Xe2bNnQ6/X4+jRo70u/9tvv0Vrayvuvfdet3X0l5+fHwCgtbXVI8v7sa7t7ujoQHh4OMaOHYulS5di/fr1OHv2rHM6d+OGqqysDK2trbjjjjucw0wmE0JCQlwuxQ43LS0tEBH4+/sDGPx2dB2PXceeu7bWaltRdwwtjfvJT36Cf/zjH1i1ahVqa2vx8ccfu53+wQcfxJdffoni4mK0tbXh2LFjKCoqwq9+9as+Q6s/GhoaAPwvMH7MarXi8uXLvc5bWVkJAAgKChpyHQBw++23w2g0ory8fMjLeueddxAfH4+goCAYDAaX+3gmkwkHDx7E3Llz8fzzzyM8PBzJycloa2tzO26oWlpaAAB/+tOfXJ6LOnfu3HUJak/p2h+TJ08G4LntcNfWWm0r6o6hpTGJiYkuZzpdHn74YQB9n1WsX78e99xzD1JSUuDv74+EhAQsWrRoyM9CdbFarQDQYzg1NDQgNDS013mNRiOAaw/WeoLBYMB9992HH374wW2nlUuXLmHFihW9jj9//jwWLFiAkJAQHD16FI2NjcjKynKZZtq0aXjrrbdQVVWF9PR05OfnY8uWLX2OG4qucM/JyYFcu9Tv/HQ9LD0c7d+/HwDwwAMPAPDsdvTW1lptK+qOoaUx7e3t+Prrr7sN7+oRFhUV5Xb+U6dO4cyZM6itrYXD4cD58+eRm5uLgIAAj9R3xx13wM/Pr9vDo0ePHoXdbsfdd9/tdl4vLy98+umnHqkFuBbSBoMBf/jDH3o9uyktLe21Ozxw7YFuh8OBxx9/HOHh4TAajS7d8Kuqqpz7JCgoCBs3bsTMmTPx9ddfux03VF09OIfyppAbrbq6Gjk5OQgNDcVvf/tbAJ7bDndtrcW2op4xtDRowYIF2Lt3LxoaGtDY2Iji4mKsW7cOv/71r/sMrdWrV8Nms6G5ufm61GY0GrFmzRq88cYbePXVV9HU1ISSkhKsWrUK48aNQ2pqaq/zBgUFITExEYWFhdi1axeamppw8uTJIT0/dtddd+G1115DaWkp4uLi8O6776KxsREOhwPfffcdXn75ZTz66KNun4my2WwAgA8//BBXrlxBRUWFy725qqoqrFy5EqdPn4bdbsfx48dx7tw5xMTEuB03VEajEcuXL0deXh5yc3PR1NSEjo4OVFZW4uLFi0Ne/lCICJqbm9HZ2QkRQW1tLfLz8zFnzhx4e3ujqKjIeU/LU9vhrq2Hc1vRAN34vh/UZTC9sw4cOCCLFy+WiIgIMRgMotfrZdKkSbJ+/Xq5cuWKc7pt27ZJSEiIABCz2SwPPfSQiIgcPHhQAgMDnb0OAYhOp5MpU6bIvn37RERk69atYjabBYDcfvvtcujQIdm0aZNYLBYBIMHBwfLaa6/J66+/LsHBwQJAAgICJC8vT0REOjs7ZfPmzTJx4kTR6XQSEBAgCxYskLKysj7ru3z5sqxYsUICAwPFz89P5s6dKxkZGQJAQkND5auvvhpUW58/f17Wrl0r06dPFz8/P/H29har1SozZsyQRx99VA4fPiwiItnZ2c5t8vX1lYSEBBERSU9Pl9GjR4vVapWkpCTZvn27AJCIiAg5dOiQxMbGSkBAgHh7e8v48ePlmWeekatXr8rZs2d7HddTG5w9e1ZmzJghAMTHx0dmzpwphYWFvbZXe3u7pKeni81mEx8fHwkKCpLExEQ5deqUZGVliclkcnYv37Nnz4DabKDH55tvvilRUVFiNptFr9eLl5eXAHD2FIyOjpYNGzb0+HiGu+146aWXnMfjxIkT5cyZM7Jjxw7x9/cXAHLbbbdJeXm527buax39xd6D6lNEhvkLzUawpKQkAEBBQcENW2dubi4qKiqQk5PjHGa327Fu3Trk5uaivr4eJpPphtVDw5cax+dwt3fvXixevHjYvwdyJOO7B28i1dXVSEtL63ZdX6/Xw2azweFwwOFwMLSIaNjiPa2biMlkgk6nw65du/D999/D4XCgqqoKO3fuREZGBpKTk533GYar06dPd/vpi54+ycnJapdKRNcBz7RuIhaLBQcOHMCGDRsQGRmJlpYW+Pn5Ydq0adi0aZPL28SHq8mTJ/PSDNFNjKF1k4mLi8MHH3ygdhlERIPCy4NERKQZDC0iItIMhhYREWkGQ4uIiDSDoUVERJrB0CIiIs1gaBERkWYwtIiISDMYWkREpBkMLSIi0gyGFhERaQZDi4iINIOhRUREmsG3vKussLAQiqKoXQZRr3h80nCiCH+cSDVHjhzBhQsX1C6DiAZo0aJFapdw02JoERGRZvCeFhERaQZDi4iINIOhRUREmuEDoEDtIoiIiPrjv9rvnnRIH3D+AAAAAElFTkSuQmCC\n"
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(123)\n",
        "#X_train: X1, X_test: X2, Y_train:Y1, Y_test:Y2\n",
        "\n",
        "X1, X2, Y1, Y2 = train_test_split (X_clean_pad,Y.reshape(Y.shape[0],1),test_size=0.30)\n",
        "\n",
        "print(len(X1))\n",
        "print(len(X2))\n",
        "print(len(Y1))\n",
        "print(len(Y2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ugWsV6SeOGUb",
        "outputId": "337bb623-476c-4526-caaf-6e1dae7d190b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1120000\n",
            "480000\n",
            "1120000\n",
            "480000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "att_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),loss='binary_crossentropy',\n",
        "                metrics=['accuracy'])\n",
        "att_model\n"
      ],
      "metadata": {
        "id": "ZPKx2lDsL1AN",
        "outputId": "988bd93f-56b2-46d8-a235-5ff9f3a80a6a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.engine.sequential.Sequential at 0x7f3c56a653d0>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "att_model = att_model.fit(X1,Y1,batch_size=128,epochs=10,validation_split=0.30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gEWzOTvfOGQO",
        "outputId": "0a8ab493-30f0-430d-e495-5b51fbc9bb80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "6125/6125 [==============================] - 556s 90ms/step - loss: 0.6182 - accuracy: 0.6526 - val_loss: 0.5834 - val_accuracy: 0.6827\n",
            "Epoch 2/10\n",
            "6125/6125 [==============================] - 549s 90ms/step - loss: 0.5663 - accuracy: 0.6989 - val_loss: 0.5437 - val_accuracy: 0.7174\n",
            "Epoch 3/10\n",
            "6125/6125 [==============================] - 516s 84ms/step - loss: 0.5389 - accuracy: 0.7218 - val_loss: 0.5267 - val_accuracy: 0.7311\n",
            "Epoch 4/10\n",
            "6125/6125 [==============================] - 496s 81ms/step - loss: 0.5230 - accuracy: 0.7347 - val_loss: 0.5176 - val_accuracy: 0.7399\n",
            "Epoch 5/10\n",
            "6125/6125 [==============================] - 488s 80ms/step - loss: 0.5118 - accuracy: 0.7434 - val_loss: 0.5173 - val_accuracy: 0.7390\n",
            "Epoch 6/10\n",
            "6125/6125 [==============================] - 488s 80ms/step - loss: 0.5031 - accuracy: 0.7496 - val_loss: 0.5003 - val_accuracy: 0.7512\n",
            "Epoch 7/10\n",
            "6125/6125 [==============================] - 488s 80ms/step - loss: 0.4969 - accuracy: 0.7542 - val_loss: 0.4976 - val_accuracy: 0.7530\n",
            "Epoch 8/10\n",
            "6125/6125 [==============================] - 487s 80ms/step - loss: 0.4913 - accuracy: 0.7578 - val_loss: 0.4926 - val_accuracy: 0.7573\n",
            "Epoch 9/10\n",
            "6125/6125 [==============================] - 488s 80ms/step - loss: 0.4868 - accuracy: 0.7613 - val_loss: 0.4871 - val_accuracy: 0.7606\n",
            "Epoch 10/10\n",
            "6125/6125 [==============================] - 488s 80ms/step - loss: 0.4821 - accuracy: 0.7648 - val_loss: 0.4868 - val_accuracy: 0.7625\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "att_model.params"
      ],
      "metadata": {
        "id": "3Os8_NuIBUsk",
        "outputId": "1e79c1fe-a5f6-45aa-fac1-102ec107f1b7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'epochs': 10, 'steps': 6125, 'verbose': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "att_model.history"
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      "metadata": {
        "id": "tXcZHe_bg0XK",
        "outputId": "9c9d9665-325e-4b61-c978-a924e85a191c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': [0.6525701284408569,\n",
              "  0.6988711953163147,\n",
              "  0.7217869758605957,\n",
              "  0.7347449064254761,\n",
              "  0.7434477210044861,\n",
              "  0.7495625019073486,\n",
              "  0.7541581392288208,\n",
              "  0.7577563524246216,\n",
              "  0.7613252401351929,\n",
              "  0.7647678852081299],\n",
              " 'loss': [0.61822909116745,\n",
              "  0.5662626028060913,\n",
              "  0.538870096206665,\n",
              "  0.5230321884155273,\n",
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              "  0.49688851833343506,\n",
              "  0.4913395345211029,\n",
              "  0.48676252365112305,\n",
              "  0.4820605218410492],\n",
              " 'val_accuracy': [0.6827232241630554,\n",
              "  0.7174196243286133,\n",
              "  0.7310506105422974,\n",
              "  0.739934504032135,\n",
              "  0.7390416860580444,\n",
              "  0.7512142658233643,\n",
              "  0.7529642581939697,\n",
              "  0.7573065757751465,\n",
              "  0.7605594992637634,\n",
              "  0.7624910473823547],\n",
              " 'val_loss': [0.5833690762519836,\n",
              "  0.5437344908714294,\n",
              "  0.5266674757003784,\n",
              "  0.5175577998161316,\n",
              "  0.5172863006591797,\n",
              "  0.5003176331520081,\n",
              "  0.49763545393943787,\n",
              "  0.49264758825302124,\n",
              "  0.4871337413787842,\n",
              "  0.48677632212638855]}"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "att_model.evaluate(X2,Y2)"
      ],
      "metadata": {
        "id": "re46yT5YN8kz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "w3jp6FunN8dw"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "attention.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
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