01. cross_attention_bahdanau_attention_implementation.ipynb

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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "metadata": {
        "id": "nKBx5LFGnXHp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2Ej64XodTO85"
      },
      "outputs": [],
      "source": [
        "# downloading data\n",
        "!wget http://www.manythings.org/anki/ita-eng.zip\n",
        "!unzip ita-eng.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Processing Data"
      ],
      "metadata": {
        "id": "idCMIoXenlLJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# reading data\n",
        "with open('ita.txt', 'r', encoding=\"utf8\") as f:\n",
        "    print(f.readlines(500))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TBxjssuAuxoO",
        "outputId": "fb3081cc-504f-47e7-f66a-74d1da3151e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Hi.\\tCiao!\\tCC-BY 2.0 (France) Attribution: tatoeba.org #538123 (CM) & #607364 (Cero)\\n', 'Hi.\\tCiao.\\tCC-BY 2.0 (France) Attribution: tatoeba.org #538123 (CM) & #4522287 (Guybrush88)\\n', 'Run!\\tCorri!\\tCC-BY 2.0 (France) Attribution: tatoeba.org #906328 (papabear) & #906347 (Guybrush88)\\n', 'Run!\\tCorra!\\tCC-BY 2.0 (France) Attribution: tatoeba.org #906328 (papabear) & #906348 (Guybrush88)\\n', 'Run!\\tCorrete!\\tCC-BY 2.0 (France) Attribution: tatoeba.org #906328 (papabear) & #906350 (Guybrush88)\\n', 'Who?\\tChi?\\tCC-BY 2.0 (France) Attribution: tatoeba.org #2083030 (CK) & #2126402 (Guybrush88)\\n']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating dataframe with english , italian sentences\n",
        "with open('ita.txt', 'r', encoding=\"utf8\") as f:\n",
        "    eng=[]\n",
        "    ita=[]\n",
        "    for i in f.readlines():\n",
        "      a = i.split(\"\\t\")\n",
        "      eng.append(a[0])\n",
        "      ita.append(a[1])\n",
        "data = pd.DataFrame()\n",
        "data['english'] = eng\n",
        "data['italian'] = ita\n",
        "data"
      ],
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              "                                                  english  \\\n",
              "0                                                     Hi.   \n",
              "1                                                     Hi.   \n",
              "2                                                    Run!   \n",
              "3                                                    Run!   \n",
              "4                                                    Run!   \n",
              "...                                                   ...   \n",
              "362856  I know that adding sentences only in your nati...   \n",
              "362857  I know that adding sentences only in your nati...   \n",
              "362858  I know that adding sentences only in your nati...   \n",
              "362859  Doubtless there exists in this world precisely...   \n",
              "362860  Doubtless there exists in this world precisely...   \n",
              "\n",
              "                                                  italian  \n",
              "0                                                   Ciao!  \n",
              "1                                                   Ciao.  \n",
              "2                                                  Corri!  \n",
              "3                                                  Corra!  \n",
              "4                                                Correte!  \n",
              "...                                                   ...  \n",
              "362856  So che aggiungere frasi soltanto nella sua lin...  \n",
              "362857  So che aggiungere frasi solamente nella sua li...  \n",
              "362858  So che aggiungere frasi solamente nella sua li...  \n",
              "362859  Senza dubbio esiste in questo mondo proprio la...  \n",
              "362860  Senza dubbio esiste in questo mondo proprio la...  \n",
              "\n",
              "[362861 rows x 2 columns]"
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              "      <td>Run!</td>\n",
              "      <td>Corri!</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Run!</td>\n",
              "      <td>Corra!</td>\n",
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              "      <th>4</th>\n",
              "      <td>Run!</td>\n",
              "      <td>Correte!</td>\n",
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      "source": [
        "def decontractions(phrase): #https://stackoverflow.com/questions/19790188/expanding-english-language-contractions-in-python/47091490#47091490\n",
        "    phrase = re.sub(r\"won\\'t\", \"will not\", phrase)\n",
        "    phrase = re.sub(r\"can\\'t\", \"can not\", phrase)\n",
        "    phrase = re.sub(r\"won\\’t\", \"will not\", phrase)\n",
        "    phrase = re.sub(r\"can\\’t\", \"can not\", phrase)\n",
        "    phrase = re.sub(r\"n\\'t\", \" not\", phrase)\n",
        "    phrase = re.sub(r\"\\'re\", \" are\", phrase)\n",
        "    phrase = re.sub(r\"\\'s\", \" is\", phrase)\n",
        "    phrase = re.sub(r\"\\'d\", \" would\", phrase)\n",
        "    phrase = re.sub(r\"\\'ll\", \" will\", phrase)\n",
        "    phrase = re.sub(r\"\\'t\", \" not\", phrase)\n",
        "    phrase = re.sub(r\"\\'ve\", \" have\", phrase)\n",
        "    phrase = re.sub(r\"\\'m\", \" am\", phrase)\n",
        "    phrase = re.sub(r\"n\\’t\", \" not\", phrase)\n",
        "    phrase = re.sub(r\"\\’re\", \" are\", phrase)\n",
        "    phrase = re.sub(r\"\\’s\", \" is\", phrase)\n",
        "    phrase = re.sub(r\"\\’d\", \" would\", phrase)\n",
        "    phrase = re.sub(r\"\\’ll\", \" will\", phrase)\n",
        "    phrase = re.sub(r\"\\’t\", \" not\", phrase)\n",
        "    phrase = re.sub(r\"\\’ve\", \" have\", phrase)\n",
        "    phrase = re.sub(r\"\\’m\", \" am\", phrase)\n",
        "    return phrase"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_eng(text):\n",
        "    text = decontractions(text)\n",
        "    text = re.sub('[^A-Za-z0-9 ]+', '', text)\n",
        "    text = text.lower()\n",
        "    return text"
      ],
      "metadata": {
        "id": "7ro9frBoxUZU"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "def preprocess_ita(text):\n",
        "    text = decontractions(text)\n",
        "    text = re.sub('[$)\\?\"’.°!;\\'€%:,(/]', '', text)\n",
        "    text = re.sub('\\u200b', ' ', text)\n",
        "    text = re.sub('\\xa0', ' ', text)\n",
        "    text = re.sub('-', ' ', text)\n",
        "    text = text.lower()\n",
        "    return text"
      ],
      "metadata": {
        "id": "QU027YfrxVN2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['english'] = data['english'].apply(preprocess_eng)\n",
        "data['italian'] = data['italian'].apply(preprocess_ita)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
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        "id": "aDwh-dptxZAb",
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      },
      "execution_count": null,
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          "data": {
            "text/plain": [
              "  english  italian\n",
              "0      hi     ciao\n",
              "1      hi     ciao\n",
              "2     run    corri\n",
              "3     run    corra\n",
              "4     run  correte"
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      "cell_type": "code",
      "source": [
        "# getting length of sentences to select max_length\n",
        "def length_text(data):\n",
        "  len_data = []\n",
        "  for i in data:\n",
        "    len_data.append(len(i.split()))\n",
        "  return len_data"
      ],
      "metadata": {
        "id": "v6fTplFoy_Tf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ita_lengths = length_text(data['italian'].values)\n",
        "eng_lengths = length_text(data['english'].values)"
      ],
      "metadata": {
        "id": "IRFw5JSbzV7v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.quantile(ita_lengths , [0,0.25,0.5,0.75,0.9,0.95,0.99])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fm9tekaxzhPO",
        "outputId": "7c6061f1-9799-4684-8064-b321b6580ab5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.,  4.,  5.,  7.,  8.,  9., 12.])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.quantile(eng_lengths , [0,0.25,0.5,0.75,0.9,0.95,0.99])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gi4hK0Vp0KdG",
        "outputId": "aa932b56-5979-4298-b57a-a47b558d1467"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.,  4.,  6.,  7.,  8.,  9., 12.])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.quantile(ita_lengths , np.arange(.99,1,0.001)) # getting percentile value between 0.99 to 1.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QQbR-wQ60ap8",
        "outputId": "95c91e31-be8b-4a63-fb28-c3833ed91fe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12., 12., 12., 13., 13., 13., 14., 15., 16., 22., 92.])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.quantile(eng_lengths , np.arange(.99,1,0.001))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6acz0qOg0jtQ",
        "outputId": "b779d7e2-08cc-421f-f8d7-753f34e19586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 12.,  12.,  13.,  13.,  13.,  14.,  14.,  15.,  16.,  25., 101.])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IypVGvYKREOL",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "ita_lengths = data['italian'].str.split().apply(len)\n",
        "eng_lengths = data['english'].str.split().apply(len)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "frL1wvLwSz7_"
      },
      "outputs": [],
      "source": [
        "# selecting maximum input length of 20 words\n",
        "\n",
        "data['italian_len'] = data['italian'].str.split().apply(len)\n",
        "data = data[data['italian_len'] < 20]\n",
        "\n",
        "data['english_len'] = data['english'].str.split().apply(len)\n",
        "data = data[data['english_len'] < 20]\n",
        "\n",
        "data['english_inp'] = '<start> ' + data['english'].astype(str) # decoder input starts with <start> token\n",
        "data['english_out'] = data['english'].astype(str) + ' <end>'   # decoder output ends with <end> token\n",
        "\n",
        "data = data.drop(['english','italian_len','english_len'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.english_inp.values[15000])\n",
        "print(data.english_out.values[15000])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YX8QRj7zpj1p",
        "outputId": "75520544-4fc2-4772-8533-2ba05194f935"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<start> let tom drive\n",
            "let tom drive <end>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "svobsg_yXbux",
        "outputId": "6171ca36-eff7-4df8-c4e7-1118b9d31391"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            italian                             english_inp  \\\n",
              "99788          tom sembra devastato            <start> tom looks devastated   \n",
              "59360    aggiungete una spiegazione              <start> add an explanation   \n",
              "250904  avevo tutto sotto controllo  <start> i had everything under control   \n",
              "12678                   fu lapidato                   <start> he was stoned   \n",
              "123290      io non ve lo posso dare        <start> i can not give it to you   \n",
              "\n",
              "                                 english_out  \n",
              "99788             tom looks devastated <end>  \n",
              "59360               add an explanation <end>  \n",
              "250904  i had everything under control <end>  \n",
              "12678                    he was stoned <end>  \n",
              "123290        i can not give it to you <end>  "
            ],
            "text/html": [
              "\n",
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              "    <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>italian</th>\n",
              "      <th>english_inp</th>\n",
              "      <th>english_out</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>99788</th>\n",
              "      <td>tom sembra devastato</td>\n",
              "      <td>&lt;start&gt; tom looks devastated</td>\n",
              "      <td>tom looks devastated &lt;end&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59360</th>\n",
              "      <td>aggiungete una spiegazione</td>\n",
              "      <td>&lt;start&gt; add an explanation</td>\n",
              "      <td>add an explanation &lt;end&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250904</th>\n",
              "      <td>avevo tutto sotto controllo</td>\n",
              "      <td>&lt;start&gt; i had everything under control</td>\n",
              "      <td>i had everything under control &lt;end&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12678</th>\n",
              "      <td>fu lapidato</td>\n",
              "      <td>&lt;start&gt; he was stoned</td>\n",
              "      <td>he was stoned &lt;end&gt;</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123290</th>\n",
              "      <td>io non ve lo posso dare</td>\n",
              "      <td>&lt;start&gt; i can not give it to you</td>\n",
              "      <td>i can not give it to you &lt;end&gt;</td>\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b680214f-c05a-490e-86b8-5f9ea8d4c9fa');\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",
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              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "data.sample(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UG50P52vhMu8"
      },
      "outputs": [],
      "source": [
        "# train test split\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "trainval, test = train_test_split(data, test_size=0.003) # for test set getting appox. 1000 sentences\n",
        "train, val = train_test_split(trainval, test_size=0.15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9mp9dzm1hwv-",
        "outputId": "3d442a19-5798-433c-a129-727cd15038b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(307077, 3) (54191, 3) (1088, 3)\n"
          ]
        }
      ],
      "source": [
        "print(train.shape, val.shape, test.shape)\n",
        "train.iloc[0]['english_inp']= str(train.iloc[0]['english_inp'])+' <end>'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jAMqqi8GaGvq"
      },
      "outputs": [],
      "source": [
        "# tokenizing\n",
        "\n",
        "tknizer_ita = Tokenizer()\n",
        "tknizer_ita.fit_on_texts(train['italian'].values)\n",
        "tknizer_eng = Tokenizer(filters='!\"#$%&()*+,-./:;=?@[\\\\]^_`{|}~\\t\\n')\n",
        "tknizer_eng.fit_on_texts(train['english_inp'].values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L1AC6vShbsyf",
        "outputId": "092a746f-6c06-4260-b904-9c52890c3b22"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13335\n",
            "27402\n"
          ]
        }
      ],
      "source": [
        "vocab_size_eng=len(tknizer_eng.word_index.keys())\n",
        "print(vocab_size_eng)\n",
        "vocab_size_ita=len(tknizer_ita.word_index.keys())\n",
        "print(vocab_size_ita)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "poK5koaDb7KO",
        "outputId": "f556b762-07bd-48d6-ec7c-34979f4b14be"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 10668)"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ],
      "source": [
        "tknizer_eng.word_index['<start>'], tknizer_eng.word_index['<end>']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://www.dropbox.com/s/ddkmtqz01jc024u/glove.6B.100d.txt"
      ],
      "metadata": {
        "id": "r0gAaYHDniUu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4I8sqk4nefgN"
      },
      "outputs": [],
      "source": [
        "# embedding matrix using glove 100d embeddings\n",
        "\n",
        "embeddings_index = dict()\n",
        "f = open('glove.6B.100d.txt')\n",
        "for line in f:\n",
        "    values = line.split()\n",
        "    word = values[0]\n",
        "    coefs = np.asarray(values[1:], dtype='float32')\n",
        "    embeddings_index[word] = coefs\n",
        "f.close()\n",
        "\n",
        "embedding_matrix = np.zeros((vocab_size_eng+1, 100))\n",
        "for word, i in tknizer_eng.word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embedding_matrix[i] = embedding_vector"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_matrix.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BNmkA74APKU",
        "outputId": "149eeaa3-6b9e-41ed-f3f4-b22d9726819d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13336, 100)"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset Loader:"
      ],
      "metadata": {
        "id": "tJDATf05rIFX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7mBXRd_sus3C"
      },
      "outputs": [],
      "source": [
        "class Dataset:\n",
        "    def __init__(self, data, tknizer_ita, tknizer_eng, max_len):\n",
        "        self.encoder_inps = data['italian'].values\n",
        "        self.decoder_inps = data['english_inp'].values\n",
        "        self.decoder_outs = data['english_out'].values\n",
        "        self.tknizer_eng = tknizer_eng\n",
        "        self.tknizer_ita = tknizer_ita\n",
        "        self.max_len = max_len\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        self.encoder_seq = self.tknizer_ita.texts_to_sequences([self.encoder_inps[i]])  # tokenizing input italian sentence\n",
        "        self.decoder_inp_seq = self.tknizer_eng.texts_to_sequences([self.decoder_inps[i]]) # tokenizing decoder input \n",
        "        self.decoder_out_seq = self.tknizer_eng.texts_to_sequences([self.decoder_outs[i]]) # tokenizing decoder output\n",
        "\n",
        "        self.encoder_seq = pad_sequences(self.encoder_seq, maxlen=self.max_len, dtype='int32', padding='post')\n",
        "        self.decoder_inp_seq = pad_sequences(self.decoder_inp_seq, maxlen=self.max_len, dtype='int32', padding='post')\n",
        "        self.decoder_out_seq = pad_sequences(self.decoder_out_seq, maxlen=self.max_len, dtype='int32', padding='post')\n",
        "        return self.encoder_seq, self.decoder_inp_seq, self.decoder_out_seq\n",
        "\n",
        "    def __len__(self): \n",
        "        return len(self.encoder_inps)\n",
        "\n",
        "    \n",
        "class Dataloder(tf.keras.utils.Sequence):    \n",
        "    def __init__(self, dataset, batch_size=1):\n",
        "        self.dataset = dataset\n",
        "        self.batch_size = batch_size\n",
        "        self.indexes = np.arange(len(self.dataset.encoder_inps))\n",
        "\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        start = i * self.batch_size\n",
        "        stop = (i + 1) * self.batch_size\n",
        "        data = []\n",
        "        for j in range(start, stop):\n",
        "            data.append(self.dataset[j])\n",
        "\n",
        "        batch = [np.squeeze(np.stack(samples, axis=1), axis=0) for samples in zip(*data)]\n",
        "        \n",
        "        return tuple([[batch[0],batch[1]],batch[2]]) # input is made of italian sent for encoder and eng. input for decoder, output is decoder output eng sent\n",
        "\n",
        "    def __len__(self): \n",
        "        return len(self.indexes) // self.batch_size\n",
        "\n",
        "    def on_epoch_end(self):\n",
        "        self.indexes = np.random.permutation(self.indexes)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building Model:"
      ],
      "metadata": {
        "id": "LNhofLP204vr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
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        "\n",
        "ref: https://guillaumegenthial.github.io/sequence-to-sequence.html"
      ],
      "metadata": {
        "id": "5x6nSO3J0Kvy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Encoder layer:"
      ],
      "metadata": {
        "id": "P280QZRsrEaC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9Kb4esWRNSUg"
      },
      "outputs": [],
      "source": [
        "class Encoder(tf.keras.layers.Layer):\n",
        "    '''class that enocodes input sentence using lstm and returns encoded vector of shape [batch_size,input_length,lstm_units]'''\n",
        "    def __init__(self, vocab_size, embedding_dim_enc, input_length, enc_units, dropout=0.0, recurrent_dropout=0.0):\n",
        "      super().__init__()\n",
        "      self.vocab_size = vocab_size\n",
        "      self.embedding_dim_enc = embedding_dim_enc\n",
        "      self.input_length = input_length\n",
        "      self.enc_units= enc_units\n",
        "      self.dropout = dropout \n",
        "      self.recurrent_dropout = recurrent_dropout \n",
        "\n",
        "    def get_config(self):\n",
        "      config = super().get_config()\n",
        "      config.update({'vocab_size': self.vocab_size, 'embedding_dim_enc': self.embedding_dim_enc,\\\n",
        "                     'input_length': self.input_length, 'enc_units': self.enc_units})\n",
        "      return config\n",
        "\n",
        "    def build(self, input_shape):\n",
        "      self.embedding = Embedding(input_dim=self.vocab_size, output_dim=self.embedding_dim_enc, input_length=self.input_length,\n",
        "                          mask_zero=True, name=\"embedding_layer_encoder\") \n",
        "      self.lstm = LSTM(self.enc_units, return_state=True, return_sequences=True,dropout=self.dropout,\\\n",
        "                        recurrent_dropout=self.recurrent_dropout ,name=\"Encoder_LSTM\")\n",
        "      \n",
        "    def call(self, input_sentances, training=True):\n",
        "      '''input sentence is embeded and then passed to lstm'''\n",
        "      input_embedd = self.embedding(input_sentances) # [b, max_len, embed-size]\n",
        "      self.lstm_output, _, _ = self.lstm(input_embedd) # [b, max_len, lstm-units], \n",
        "      return self.lstm_output\n",
        "    \n",
        "    def initialize_states(self,batch_size):\n",
        "      '''Given a batch size it will return intial hidden state and intial cell state'''\n",
        "      return tf.zeros([batch_size, self.enc_units ]), tf.zeros([batch_size, self.enc_units ])\n",
        "\n",
        "    def get_states(self):\n",
        "      return self.lstm_state_h, self.lstm_state_c"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking code\n",
        "\n",
        "vocab_size = 150\n",
        "embedding_dim_enc = 50\n",
        "input_length = 10\n",
        "enc_units= 32\n",
        "dropout = 0.3\n",
        "recurrent_dropout = 0.3\n",
        "batch_size = 16\n",
        "\n",
        "input_sentances = tf.random.uniform(shape=[batch_size,input_length])\n",
        "\n",
        "e = Encoder(vocab_size, embedding_dim_enc, input_length, enc_units, dropout, recurrent_dropout)\n",
        "e(input_sentances).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WabI2lairy-n",
        "outputId": "b46752f1-4789-4c89-8d8f-dde1b49ff04b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([16, 10, 32])"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Attention Mechanism Layer:"
      ],
      "metadata": {
        "id": "qAjT4vcjrb52"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ab5SNdPZLlur"
      },
      "outputs": [],
      "source": [
        "class AttentionMechanism(tf.keras.layers.Layer):\n",
        "  '''Class the calculates attention weights and corresponding weighted vector using simple dot product operation.'''\n",
        "  def __init__(self, initializer = tf.keras.initializers.GlorotUniform() ):\n",
        "    super().__init__()\n",
        "    self.initializer = initializer\n",
        "\n",
        "  def get_config(self):\n",
        "    config = super().get_config()\n",
        "    config.update({'initializer': self.initializer })\n",
        "    return config\n",
        "\n",
        "  def call(self,decoder_hidden_state,encoder_output):\n",
        "    '''decoder input is transformed to match encoder output dimension and attention weights are calculated\n",
        "    based on similarity using dot products and weighted sum of encoder hidden state vector is returned as context vector\n",
        "    to be used by decoder'''\n",
        "    initializer = self.initializer\n",
        "    # initializing decoder transformation matrix\n",
        "    values = initializer(shape=(decoder_hidden_state.shape[2],encoder_output.shape[2])) # [dec_embed_dim, encoder_lstm_units]\n",
        "    # tranforming decoder input\n",
        "    similarity1 = tf.matmul(decoder_hidden_state,values) # [b,1,dec_embed_dim] X [dec_embed_dim, encoder_lstm_units] = [b,1,encoder_lstm_units]\n",
        "    # finding similarity score\n",
        "    similarity = tf.matmul(similarity1,encoder_output, transpose_b=True) # [b,1,encoder_lstm_units] X [b,encoder_lstm_units,max_len] = [b,1,max_len]\n",
        "    # normalizing scores using softmax\n",
        "    attn_weights = tf.nn.softmax( similarity,axis=-1 ) # [b,1,max_len]\n",
        "    # calculating weighted sum\n",
        "    context_vector = tf.matmul(attn_weights,encoder_output) # [b,1,max_len] X -[b,max_len,encoder_lstm_units] = [b,1,encoder_lstm_units]\n",
        "    context_vector = tf.squeeze(context_vector,axis=1) # [b,encoder_lstm_units]\n",
        "    attn_weights = tf.transpose(attn_weights, perm=[0, 2, 1]) # [b,max_len,1]\n",
        "    return context_vector, attn_weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OrxpKs1LJ7T1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f846e084-ffb2-426c-d029-0f1b6da60189"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorShape([16, 32]), TensorShape([16, 10, 1]))"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# checking code\n",
        "\n",
        "input_length=10\n",
        "batch_size=16\n",
        "encoder_lstm_units=32\n",
        "dec_embed_dim=50\n",
        "\n",
        "decoder_hidden_state=tf.random.uniform(shape=[batch_size,1,dec_embed_dim])\n",
        "encoder_output=tf.random.uniform(shape=[batch_size,input_length,encoder_lstm_units])\n",
        "\n",
        "attn=AttentionMechanism()\n",
        "c,wt = attn(decoder_hidden_state,encoder_output)\n",
        "c.shape , wt.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decoder Encoder Cross Attention:"
      ],
      "metadata": {
        "id": "ChIW-4Y_sl5D"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b7BPvFlmoPVC"
      },
      "outputs": [],
      "source": [
        "class DecoderEncoderCrossAttention(tf.keras.Model):\n",
        "  '''class that performs cross attention on decoder input, pass the attention updated input to\n",
        "  lstm and then to final dense layer having units equal to output vocab size. the decoder input is passed\n",
        "  one word at a time over batch'''\n",
        "  def __init__(self,tar_vocab_size, embedding_dim_dec, input_length, dec_units, lstm_dropout, recurrent_dropout, trainable=True):\n",
        "    super().__init__()\n",
        "    self.tar_vocab_size = tar_vocab_size\n",
        "    self.embedding_dim_dec = embedding_dim_dec\n",
        "    self.input_length = input_length\n",
        "    self.dec_units = dec_units\n",
        "    self.lstm_dropout = lstm_dropout\n",
        "    self.recurrent_dropout = recurrent_dropout \n",
        "    self.trainable = True\n",
        "\n",
        "  def get_config(self):\n",
        "      config = super().get_config()\n",
        "      config.update({\n",
        "          'tar_vocab_size': self.tar_vocab_size, 'embedding_dim_dec': self.embedding_dim_dec,\n",
        "          'input_length': self.input_length, 'dec_units': self.dec_units, 'trainable': self.trainable})\n",
        "      return config\n",
        "\n",
        "  def build(self, input_shape):\n",
        "    if self.trainable:\n",
        "      self.embedding = Embedding(input_dim=self.tar_vocab_size, output_dim=self.embedding_dim_dec, input_length=self.input_length,\n",
        "                        mask_zero=True, name=\"embedding_layer_decoder\", trainable=True)\n",
        "    else:\n",
        "      self.embedding = Embedding(input_dim=self.tar_vocab_size, output_dim=self.embedding_dim_dec, input_length=self.input_length,\n",
        "                        mask_zero=True, name=\"embedding_layer_decoder\", weights=[embedding_matrix], trainable=False)\n",
        "\n",
        "    self.lstm = LSTM(self.dec_units, return_sequences=True, return_state=True,dropout=self.lstm_dropout,recurrent_dropout=self.recurrent_dropout, name=\"Encoder_LSTM\")\n",
        "    self.attention = AttentionMechanism()\n",
        "    self.dense_layer = Dense(self.tar_vocab_size,activation=None)\n",
        "\n",
        "  def call(self,input_to_decoder, encoder_output): \n",
        "    '''takes decoder single input over batch, encoder outputs, performs cross attention and returns returns logits'''\n",
        "    # embedding decoder single input\n",
        "    target_embedd = self.embedding(input_to_decoder) # [b,1,embedding_dim_dec]\n",
        "    # getting attention updated embedding vector\n",
        "    context_vector,_ = self.attention(target_embedd,encoder_output) #[b,encoder_lstm_units]\n",
        "    # concataneting embeded input and attention updated input\n",
        "    concat_input = tf.concat([target_embedd, tf.expand_dims(context_vector, 1)], -1)  # [b,1,(embedding_dim_dec+encoder_lstm_units)] \n",
        "    # passing to lstm\n",
        "    self.lstm_output, self.lstm_state_h, self.lstm_state_c = self.lstm(concat_input) # [b,1,dec_lstm_units] , [b,1,dec_lstm_units] , [b,1,dec_lstm_units]\n",
        "    # getting logits\n",
        "    output = self.dense_layer(self.lstm_output)  # [b,1,tar_vocab_size]\n",
        "    output = tf.squeeze(output,axis=1) # [b,tar_vocab_size]\n",
        "    return output, self.lstm_state_h,self.lstm_state_c, context_vector                                                   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "88okyUfSPUXa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cafe97e-bf6a-4cc3-e27c-5681f38077a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([16, 150])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# checking code\n",
        "\n",
        "tar_vocab_size=150\n",
        "embedding_dim_dec=50\n",
        "input_length=10\n",
        "dec_units=48\n",
        "batch_size=16\n",
        "lstm_dropout = 0.5\n",
        "recurrent_dropout=0.5\n",
        "\n",
        "input_to_decoder=tf.random.uniform(shape=(batch_size,1),maxval=10,minval=0,dtype=tf.int32)\n",
        "encoder_output=tf.random.uniform(shape=[batch_size,input_length,32])\n",
        "\n",
        "d = DecoderEncoderCrossAttention(tar_vocab_size,embedding_dim_dec,input_length,dec_units,lstm_dropout,recurrent_dropout)\n",
        "o,_,_,_ = d(input_to_decoder,encoder_output)\n",
        "o.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decoder Layer:"
      ],
      "metadata": {
        "id": "IphUR5eos1bB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8Jduzh3ZP7sm"
      },
      "outputs": [],
      "source": [
        "class DecoderBlock(tf.keras.Model):\n",
        "  '''class which gives logits values for full decoder input length '''\n",
        "  def __init__(self,out_vocab_size, embedding_dim_dec, input_length, dec_units,lstm_dropout,recurrent_dropout):\n",
        "    super().__init__()\n",
        "    self.out_vocab_size = out_vocab_size\n",
        "    self.embedding_dim_dec = embedding_dim_dec\n",
        "    self.input_length = input_length\n",
        "    self.dec_units = dec_units\n",
        "    self.lstm_dropout = lstm_dropout\n",
        "    self.recurrent_dropout = recurrent_dropout\n",
        "\n",
        "  def get_config(self):\n",
        "    config = super().get_config()\n",
        "    config.update({\n",
        "        'out_vocab_size': self.out_vocab_size, 'embedding_dim_dec': self.embedding_dim_dec,\n",
        "        'input_length': self.input_length, 'dec_units': self.dec_units})\n",
        "    return config\n",
        "\n",
        "  def build(self,input_shapes):\n",
        "    self.crossattention = DecoderEncoderCrossAttention(self.out_vocab_size, self.embedding_dim_dec, self.input_length,\\\n",
        "                                                        self.dec_units ,self.lstm_dropout,self.recurrent_dropout,False)\n",
        "\n",
        "  def call(self, input_to_decoder,encoder_output):\n",
        "    # creating a empty array of length equal to input length to fill logits value\n",
        "    all_outputs = tf.TensorArray(tf.float32,size=self.input_length)\n",
        "    # iterating over individual input word\n",
        "    for timestep in range(self.input_length):\n",
        "      # getting logits value for current input word\n",
        "      output, decoder_hidden_state,decoder_cell_state,_ = self.crossattention(input_to_decoder[:,timestep:timestep+1], encoder_output)\n",
        "      all_outputs = all_outputs.write(timestep, output) #[max_len,b,tar_vocab_size]\n",
        "    all_outputs = tf.transpose(all_outputs.stack(), [1,0,2]) #[b,max_len,tar_vocab_size]\n",
        "    return all_outputs "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking code\n",
        "\n",
        "tar_vocab_size=150 \n",
        "embedding_dim_dec=50\n",
        "input_length=10\n",
        "dec_units=48\n",
        "enc_units = 32\n",
        "batch_size=16\n",
        "lstm_dropout=0.4\n",
        "recurrent_dropout=0.4\n",
        "\n",
        "input_to_decoder=tf.random.uniform(shape=(batch_size,input_length),maxval=10,minval=0,dtype=tf.int32)\n",
        "encoder_output=tf.random.uniform(shape=[batch_size,input_length,enc_units])\n",
        "\n",
        "d =  DecoderBlock(tar_vocab_size,embedding_dim_dec,input_length,dec_units,lstm_dropout,recurrent_dropout)\n",
        "d(input_to_decoder,encoder_output).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KclSF4i_l6m-",
        "outputId": "942c1f5b-d899-471e-f772-dac3281ee7ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([16, 10, 150])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model:"
      ],
      "metadata": {
        "id": "ImkfLXpts7CZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iqypHJSjy9qD"
      },
      "outputs": [],
      "source": [
        "class TranslationModel(tf.keras.Model):\n",
        "\n",
        "  def __init__(self, encoder_inputs_length,decoder_inputs_length, vocab_size_ita,vocab_size_eng,\\\n",
        "                embedding_dim_enc,embedding_dim_dec,enc_units,dec_units,lstm_dropout,recurrent_dropout):\n",
        "    super().__init__()\n",
        "    self.encoder_inputs_length = encoder_inputs_length\n",
        "    self.decoder_inputs_length = decoder_inputs_length\n",
        "    self.vocab_size_ita = vocab_size_ita\n",
        "    self.vocab_size_eng = vocab_size_eng\n",
        "    self.embedding_dim_enc = embedding_dim_enc\n",
        "    self.embedding_dim_dec = embedding_dim_dec\n",
        "    self.enc_units = enc_units\n",
        "    self.dec_units = dec_units\n",
        "    self.lstm_dropout = lstm_dropout\n",
        "    self.recurrent_dropout = recurrent_dropout\n",
        "\n",
        "  def get_config(self):\n",
        "    config = super().get_config()\n",
        "    config.update({'encoder_inputs_length': self.encoder_inputs_length, 'decoder_inputs_length': self.decoder_inputs_length,\n",
        "        'vocab_size_ita': self.vocab_size_ita, 'vocab_size_eng': self.vocab_size_eng, 'embedding_dim_enc': self.embedding_dim_enc,\n",
        "        'embedding_dim_dec': self.embedding_dim_dec , 'enc_units': self.enc_units, 'dec_units': self.dec_units, 'att_units': self.att_units})\n",
        "    return config\n",
        "\n",
        "  def build(self,input_shapes):\n",
        "      self.encoder = Encoder(self.vocab_size_ita+1, self.embedding_dim_enc, self.encoder_inputs_length,\\\n",
        "                             self.enc_units,self.lstm_dropout,self.recurrent_dropout)\n",
        "      self.decoder = DecoderBlock(self.vocab_size_eng+1, self.embedding_dim_dec, decoder_inputs_length,\\\n",
        "                                  self.lstm_dropout,self.recurrent_dropout)\n",
        "      \n",
        "  def call(self, data):\n",
        "      input,output = data[0], data[1]\n",
        "      #passing input to encoder\n",
        "      encoder_output = self.encoder(input) #[b,max_len,encoder_lstm_units]\n",
        "      #passing output to decoder\n",
        "      decoder_output = self.decoder(output, encoder_output) #[b,max_len,tar_vocab_size] #logits\n",
        "      return decoder_output"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loss Function:"
      ],
      "metadata": {
        "id": "Fidzi9Ams_ng"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z6AokS4N84R2"
      },
      "outputs": [],
      "source": [
        "# https://www.tensorflow.org/tutorials/text/image_captioning#model\n",
        "\n",
        "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n",
        "    from_logits=True, reduction='none')\n",
        "\n",
        "def loss_function(real, pred):\n",
        "    \"\"\" Custom loss function that will not consider the loss for padded zero.\"\"\"\n",
        "    mask = tf.math.logical_not(tf.math.equal(real, 0))\n",
        "    loss_ = loss_object(real, pred)\n",
        "    mask = tf.cast(mask, dtype=loss_.dtype)\n",
        "    loss_ *= mask\n",
        "    return tf.reduce_mean(loss_)\n",
        "\n",
        "def masked_acc(labels, preds):\n",
        "  mask = tf.cast(labels!=0, tf.float32)\n",
        "  preds = tf.argmax(preds, axis=-1)\n",
        "  labels = tf.cast(labels, tf.int64)\n",
        "  match = tf.cast(preds == labels, mask.dtype)\n",
        "  acc = tf.reduce_sum(match*mask)/tf.reduce_sum(mask)\n",
        "  return acc"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Callbacks:"
      ],
      "metadata": {
        "id": "xgdBekgItS9h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from itertools import combinations\n",
        "import os\n",
        "import datetime\n",
        "log_dir = os.path.join('/content/drive/MyDrive/cross_attention/',\"logs\",'model','fits', datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir,histogram_freq=1,write_graph=True)\n",
        "\n",
        "modelsave_wt = tf.keras.callbacks.ModelCheckpoint(\n",
        "                     filepath='/content/drive/MyDrive/cross_attention/model.{epoch:02d}-{masked_acc:.4f}.h5', \n",
        "                     save_freq='epoch', verbose=1, monitor='masked_acc', \n",
        "                     save_weights_only=True, save_best_only=True\n",
        "                 ) "
      ],
      "metadata": {
        "id": "ByVgmrHNjWQO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selected Hyperparameters:"
      ],
      "metadata": {
        "id": "jarUOOF8tveQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#vocab_size_ita=27260\n",
        "#vocab_size_eng=13356"
      ],
      "metadata": {
        "id": "9SKneqQJt6a0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JJ60N_XTB-80"
      },
      "outputs": [],
      "source": [
        "# after trying different values, following were selected\n",
        "\n",
        "encoder_inputs_length = 20\n",
        "decoder_inputs_length = 20\n",
        "vocab_size_ita = vocab_size_ita\n",
        "vocab_size_eng = vocab_size_eng\n",
        "embedding_dim_enc = 100\n",
        "embedding_dim_dec = 100\n",
        "enc_units = 128\n",
        "dec_units = 128\n",
        "lstm_dropout = 0.2\n",
        "recurrent_dropout = 0.2\n",
        "optimizer = tf.keras.optimizers.Adam()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Loader:"
      ],
      "metadata": {
        "id": "xhaBrnzxuFbY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size=1024\n",
        "\n",
        "train_dataset = Dataset(train, tknizer_ita, tknizer_eng, 20)\n",
        "val_dataset  = Dataset(val, tknizer_ita, tknizer_eng, 20)\n",
        "test_dataset  = Dataset(test, tknizer_ita, tknizer_eng, 20)\n",
        "\n",
        "train_dataloader = Dataloder(train_dataset, batch_size=batch_size)\n",
        "val_dataloader = Dataloder(val_dataset, batch_size=batch_size)\n",
        "test_dataloader = Dataloder(test_dataset, batch_size=batch_size)\n",
        "\n",
        "train_steps=train.shape[0]//batch_size\n",
        "valid_steps=val.shape[0]//batch_size"
      ],
      "metadata": {
        "id": "l5zitOnljwSA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training:"
      ],
      "metadata": {
        "id": "o-9roTDYuJnE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model  = TranslationModel(encoder_inputs_length,decoder_inputs_length, vocab_size_ita,vocab_size_eng,embedding_dim_enc,\\\n",
        "                          embedding_dim_dec,enc_units,dec_units, lstm_dropout, recurrent_dropout)\n",
        "\n",
        "model.compile(optimizer=optimizer, loss=loss_function, metrics=[masked_acc])"
      ],
      "metadata": {
        "id": "bY44-saA-FNa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataloader, steps_per_epoch=train_steps, epochs=10, validation_data=val_dataloader, validation_steps=valid_steps,\\\n",
        "          callbacks=[modelsave_wt,tensorboard_callback])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4cmHvCDqFgLN",
        "outputId": "797d1c33-7502-492b-fc92-51b2c6d5da45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer Encoder_LSTM will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer Encoder_LSTM will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "/usr/local/lib/python3.8/dist-packages/keras/initializers/initializers_v2.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values  each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initalizer instance more than once.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "296/296 [==============================] - ETA: 0s - loss: 1.9879 - masked_acc: 0.1504\n",
            "Epoch 1: masked_acc improved from 0.14619 to 0.15041, saving model to /content/drive/MyDrive/cross_attention/model.01-0.1504.h5\n",
            "296/296 [==============================] - 292s 748ms/step - loss: 1.9879 - masked_acc: 0.1504 - val_loss: 1.6638 - val_masked_acc: 0.1911\n",
            "Epoch 2/10\n",
            "296/296 [==============================] - ETA: 0s - loss: 1.5426 - masked_acc: 0.2328\n",
            "Epoch 2: masked_acc improved from 0.15041 to 0.23276, saving model to /content/drive/MyDrive/cross_attention/model.02-0.2328.h5\n",
            "296/296 [==============================] - 197s 665ms/step - loss: 1.5426 - masked_acc: 0.2328 - val_loss: 1.4218 - val_masked_acc: 0.2831\n",
            "Epoch 3/10\n",
            "296/296 [==============================] - ETA: 0s - loss: 1.3747 - masked_acc: 0.3093\n",
            "Epoch 3: masked_acc improved from 0.23276 to 0.30925, saving model to /content/drive/MyDrive/cross_attention/model.03-0.3093.h5\n",
            "296/296 [==============================] - 195s 659ms/step - loss: 1.3747 - masked_acc: 0.3093 - val_loss: 1.3202 - val_masked_acc: 0.3340\n",
            "Epoch 4/10\n",
            "296/296 [==============================] - ETA: 0s - loss: 1.2947 - masked_acc: 0.3424\n",
            "Epoch 4: masked_acc improved from 0.30925 to 0.34244, saving model to /content/drive/MyDrive/cross_attention/model.04-0.3424.h5\n",
            "296/296 [==============================] - 193s 652ms/step - loss: 1.2947 - masked_acc: 0.3424 - val_loss: 1.2543 - val_masked_acc: 0.3572\n",
            "Epoch 5/10\n",
            "296/296 [==============================] - ETA: 0s - loss: 1.2362 - masked_acc: 0.3634\n",
            "Epoch 5: masked_acc improved from 0.34244 to 0.36345, saving model to /content/drive/MyDrive/cross_attention/model.05-0.3634.h5\n",
            "296/296 [==============================] - 196s 661ms/step - loss: 1.2362 - masked_acc: 0.3634 - val_loss: 1.2015 - val_masked_acc: 0.3737\n",
            "Epoch 6/10\n",
            "296/296 [==============================] - ETA: 0s - loss: 1.1868 - masked_acc: 0.3822\n",
            "Epoch 6: masked_acc improved from 0.36345 to 0.38222, saving model to /content/drive/MyDrive/cross_attention/model.06-0.3822.h5\n",
            "296/296 [==============================] - 194s 656ms/step - loss: 1.1868 - masked_acc: 0.3822 - val_loss: 1.1559 - val_masked_acc: 0.3972\n",
            "Epoch 7/10\n",
            "296/296 [==============================] - ETA: 0s - loss: 1.1442 - masked_acc: 0.4000\n",
            "Epoch 7: masked_acc improved from 0.38222 to 0.40001, saving model to /content/drive/MyDrive/cross_attention/model.07-0.4000.h5\n",
            "296/296 [==============================] - 191s 645ms/step - loss: 1.1442 - masked_acc: 0.4000 - val_loss: 1.1166 - val_masked_acc: 0.4130\n",
            "Epoch 8/10\n",
            "296/296 [==============================] - ETA: 0s - loss: 1.1061 - masked_acc: 0.4158\n",
            "Epoch 8: masked_acc improved from 0.40001 to 0.41582, saving model to /content/drive/MyDrive/cross_attention/model.08-0.4158.h5\n",
            "296/296 [==============================] - 194s 654ms/step - loss: 1.1061 - masked_acc: 0.4158 - val_loss: 1.0793 - val_masked_acc: 0.4311\n",
            "Epoch 9/10\n",
            "296/296 [==============================] - ETA: 0s - loss: 1.0695 - masked_acc: 0.4324\n",
            "Epoch 9: masked_acc improved from 0.41582 to 0.43236, saving model to /content/drive/MyDrive/cross_attention/model.09-0.4324.h5\n",
            "296/296 [==============================] - 195s 658ms/step - loss: 1.0695 - masked_acc: 0.4324 - val_loss: 1.0414 - val_masked_acc: 0.4501\n",
            "Epoch 10/10\n",
            "296/296 [==============================] - ETA: 0s - loss: 1.0310 - masked_acc: 0.4501\n",
            "Epoch 10: masked_acc improved from 0.43236 to 0.45006, saving model to /content/drive/MyDrive/cross_attention/model.10-0.4501.h5\n",
            "296/296 [==============================] - 200s 677ms/step - loss: 1.0310 - masked_acc: 0.4501 - val_loss: 1.0026 - val_masked_acc: 0.4677\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f0db44b4fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataloader, steps_per_epoch=train_steps, epochs=20, validation_data=val_dataloader, validation_steps=valid_steps,\\\n",
        "          callbacks=[modelsave_wt,tensorboard_callback],initial_epoch=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mk98ChmsOvsf",
        "outputId": "9fe2f224-b9a6-4933-b659-d9d92b4f30a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 11/20\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.9896 - masked_acc: 0.4684\n",
            "Epoch 11: masked_acc improved from 0.45006 to 0.46839, saving model to /content/drive/MyDrive/cross_attention/model.11-0.4684.h5\n",
            "296/296 [==============================] - 197s 656ms/step - loss: 0.9896 - masked_acc: 0.4684 - val_loss: 0.9552 - val_masked_acc: 0.4896\n",
            "Epoch 12/20\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.9457 - masked_acc: 0.4873\n",
            "Epoch 12: masked_acc improved from 0.46839 to 0.48731, saving model to /content/drive/MyDrive/cross_attention/model.12-0.4873.h5\n",
            "296/296 [==============================] - 193s 651ms/step - loss: 0.9457 - masked_acc: 0.4873 - val_loss: 0.9081 - val_masked_acc: 0.5109\n",
            "Epoch 13/20\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.8992 - masked_acc: 0.5079\n",
            "Epoch 13: masked_acc improved from 0.48731 to 0.50789, saving model to /content/drive/MyDrive/cross_attention/model.13-0.5079.h5\n",
            "296/296 [==============================] - 195s 656ms/step - loss: 0.8992 - masked_acc: 0.5079 - val_loss: 0.8567 - val_masked_acc: 0.5352\n",
            "Epoch 14/20\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.8475 - masked_acc: 0.5315\n",
            "Epoch 14: masked_acc improved from 0.50789 to 0.53154, saving model to /content/drive/MyDrive/cross_attention/model.14-0.5315.h5\n",
            "296/296 [==============================] - 195s 659ms/step - loss: 0.8475 - masked_acc: 0.5315 - val_loss: 0.7992 - val_masked_acc: 0.5628\n",
            "Epoch 15/20\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.7918 - masked_acc: 0.5580\n",
            "Epoch 15: masked_acc improved from 0.53154 to 0.55796, saving model to /content/drive/MyDrive/cross_attention/model.15-0.5580.h5\n",
            "296/296 [==============================] - 194s 655ms/step - loss: 0.7918 - masked_acc: 0.5580 - val_loss: 0.7398 - val_masked_acc: 0.5911\n",
            "Epoch 16/20\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.7354 - masked_acc: 0.5856\n",
            "Epoch 16: masked_acc improved from 0.55796 to 0.58560, saving model to /content/drive/MyDrive/cross_attention/model.16-0.5856.h5\n",
            "296/296 [==============================] - 196s 661ms/step - loss: 0.7354 - masked_acc: 0.5856 - val_loss: 0.6804 - val_masked_acc: 0.6243\n",
            "Epoch 17/20\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.6817 - masked_acc: 0.6125\n",
            "Epoch 17: masked_acc improved from 0.58560 to 0.61246, saving model to /content/drive/MyDrive/cross_attention/model.17-0.6125.h5\n",
            "296/296 [==============================] - 194s 655ms/step - loss: 0.6817 - masked_acc: 0.6125 - val_loss: 0.6288 - val_masked_acc: 0.6501\n",
            "Epoch 18/20\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.6336 - masked_acc: 0.6362\n",
            "Epoch 18: masked_acc improved from 0.61246 to 0.63623, saving model to /content/drive/MyDrive/cross_attention/model.18-0.6362.h5\n",
            "296/296 [==============================] - 194s 657ms/step - loss: 0.6336 - masked_acc: 0.6362 - val_loss: 0.5833 - val_masked_acc: 0.6736\n",
            "Epoch 19/20\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.5894 - masked_acc: 0.6576\n",
            "Epoch 19: masked_acc improved from 0.63623 to 0.65760, saving model to /content/drive/MyDrive/cross_attention/model.19-0.6576.h5\n",
            "296/296 [==============================] - 195s 658ms/step - loss: 0.5894 - masked_acc: 0.6576 - val_loss: 0.5431 - val_masked_acc: 0.6940\n",
            "Epoch 20/20\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.5522 - masked_acc: 0.6756\n",
            "Epoch 20: masked_acc improved from 0.65760 to 0.67557, saving model to /content/drive/MyDrive/cross_attention/model.20-0.6756.h5\n",
            "296/296 [==============================] - 197s 664ms/step - loss: 0.5522 - masked_acc: 0.6756 - val_loss: 0.5135 - val_masked_acc: 0.7092\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f0c913a68b0>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataloader, steps_per_epoch=train_steps, epochs=30, validation_data=val_dataloader, validation_steps=valid_steps,\\\n",
        "          callbacks=[modelsave_wt,tensorboard_callback],initial_epoch=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVw2Ul52YT_o",
        "outputId": "113f2278-d869-4a18-d1a0-be0f95b5ba64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 21/30\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.5190 - masked_acc: 0.6916\n",
            "Epoch 21: masked_acc improved from 0.67557 to 0.69156, saving model to /content/drive/MyDrive/cross_attention/model.21-0.6916.h5\n",
            "296/296 [==============================] - 198s 665ms/step - loss: 0.5190 - masked_acc: 0.6916 - val_loss: 0.4794 - val_masked_acc: 0.7257\n",
            "Epoch 22/30\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.4882 - masked_acc: 0.7065\n",
            "Epoch 22: masked_acc improved from 0.69156 to 0.70654, saving model to /content/drive/MyDrive/cross_attention/model.22-0.7065.h5\n",
            "296/296 [==============================] - 197s 666ms/step - loss: 0.4882 - masked_acc: 0.7065 - val_loss: 0.4541 - val_masked_acc: 0.7388\n",
            "Epoch 23/30\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.4623 - masked_acc: 0.7191\n",
            "Epoch 23: masked_acc improved from 0.70654 to 0.71911, saving model to /content/drive/MyDrive/cross_attention/model.23-0.7191.h5\n",
            "296/296 [==============================] - 194s 656ms/step - loss: 0.4623 - masked_acc: 0.7191 - val_loss: 0.4318 - val_masked_acc: 0.7501\n",
            "Epoch 24/30\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.4396 - masked_acc: 0.7302\n",
            "Epoch 24: masked_acc improved from 0.71911 to 0.73017, saving model to /content/drive/MyDrive/cross_attention/model.24-0.7302.h5\n",
            "296/296 [==============================] - 192s 648ms/step - loss: 0.4396 - masked_acc: 0.7302 - val_loss: 0.4130 - val_masked_acc: 0.7592\n",
            "Epoch 25/30\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.4194 - masked_acc: 0.7398\n",
            "Epoch 25: masked_acc improved from 0.73017 to 0.73983, saving model to /content/drive/MyDrive/cross_attention/model.25-0.7398.h5\n",
            "296/296 [==============================] - 192s 647ms/step - loss: 0.4194 - masked_acc: 0.7398 - val_loss: 0.3975 - val_masked_acc: 0.7674\n",
            "Epoch 26/30\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.4018 - masked_acc: 0.7485\n",
            "Epoch 26: masked_acc improved from 0.73983 to 0.74851, saving model to /content/drive/MyDrive/cross_attention/model.26-0.7485.h5\n",
            "296/296 [==============================] - 191s 645ms/step - loss: 0.4018 - masked_acc: 0.7485 - val_loss: 0.3822 - val_masked_acc: 0.7748\n",
            "Epoch 27/30\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.3851 - masked_acc: 0.7563\n",
            "Epoch 27: masked_acc improved from 0.74851 to 0.75629, saving model to /content/drive/MyDrive/cross_attention/model.27-0.7563.h5\n",
            "296/296 [==============================] - 191s 645ms/step - loss: 0.3851 - masked_acc: 0.7563 - val_loss: 0.3703 - val_masked_acc: 0.7808\n",
            "Epoch 28/30\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.3707 - masked_acc: 0.7634\n",
            "Epoch 28: masked_acc improved from 0.75629 to 0.76343, saving model to /content/drive/MyDrive/cross_attention/model.28-0.7634.h5\n",
            "296/296 [==============================] - 190s 642ms/step - loss: 0.3707 - masked_acc: 0.7634 - val_loss: 0.3592 - val_masked_acc: 0.7858\n",
            "Epoch 29/30\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.3582 - masked_acc: 0.7693\n",
            "Epoch 29: masked_acc improved from 0.76343 to 0.76926, saving model to /content/drive/MyDrive/cross_attention/model.29-0.7693.h5\n",
            "296/296 [==============================] - 192s 649ms/step - loss: 0.3582 - masked_acc: 0.7693 - val_loss: 0.3495 - val_masked_acc: 0.7911\n",
            "Epoch 30/30\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.3465 - masked_acc: 0.7753\n",
            "Epoch 30: masked_acc improved from 0.76926 to 0.77534, saving model to /content/drive/MyDrive/cross_attention/model.30-0.7753.h5\n",
            "296/296 [==============================] - 190s 640ms/step - loss: 0.3465 - masked_acc: 0.7753 - val_loss: 0.3406 - val_masked_acc: 0.7958\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f0f6291c790>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataloader, steps_per_epoch=train_steps, epochs=40, validation_data=val_dataloader, validation_steps=valid_steps,\\\n",
        "          callbacks=[modelsave_wt,tensorboard_callback],initial_epoch=30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CiU_eqtZg6ux",
        "outputId": "eff6ff40-0f3c-49ac-dc6d-b76af8b784b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 31/40\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.3364 - masked_acc: 0.7803\n",
            "Epoch 31: masked_acc improved from 0.77534 to 0.78026, saving model to /content/drive/MyDrive/cross_attention/model.31-0.7803.h5\n",
            "296/296 [==============================] - 198s 662ms/step - loss: 0.3364 - masked_acc: 0.7803 - val_loss: 0.3332 - val_masked_acc: 0.7998\n",
            "Epoch 32/40\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.3272 - masked_acc: 0.7846\n",
            "Epoch 32: masked_acc improved from 0.78026 to 0.78462, saving model to /content/drive/MyDrive/cross_attention/model.32-0.7846.h5\n",
            "296/296 [==============================] - 196s 663ms/step - loss: 0.3272 - masked_acc: 0.7846 - val_loss: 0.3257 - val_masked_acc: 0.8036\n",
            "Epoch 33/40\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.3184 - masked_acc: 0.7889\n",
            "Epoch 33: masked_acc improved from 0.78462 to 0.78893, saving model to /content/drive/MyDrive/cross_attention/model.33-0.7889.h5\n",
            "296/296 [==============================] - 194s 656ms/step - loss: 0.3184 - masked_acc: 0.7889 - val_loss: 0.3189 - val_masked_acc: 0.8065\n",
            "Epoch 34/40\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.3124 - masked_acc: 0.7917\n",
            "Epoch 34: masked_acc improved from 0.78893 to 0.79167, saving model to /content/drive/MyDrive/cross_attention/model.34-0.7917.h5\n",
            "296/296 [==============================] - 193s 653ms/step - loss: 0.3124 - masked_acc: 0.7917 - val_loss: 0.3132 - val_masked_acc: 0.8094\n",
            "Epoch 35/40\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.3029 - masked_acc: 0.7966\n",
            "Epoch 35: masked_acc improved from 0.79167 to 0.79662, saving model to /content/drive/MyDrive/cross_attention/model.35-0.7966.h5\n",
            "296/296 [==============================] - 195s 658ms/step - loss: 0.3029 - masked_acc: 0.7966 - val_loss: 0.3076 - val_masked_acc: 0.8127\n",
            "Epoch 36/40\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2963 - masked_acc: 0.7999\n",
            "Epoch 36: masked_acc improved from 0.79662 to 0.79986, saving model to /content/drive/MyDrive/cross_attention/model.36-0.7999.h5\n",
            "296/296 [==============================] - 196s 662ms/step - loss: 0.2963 - masked_acc: 0.7999 - val_loss: 0.3029 - val_masked_acc: 0.8154\n",
            "Epoch 37/40\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2900 - masked_acc: 0.8032\n",
            "Epoch 37: masked_acc improved from 0.79986 to 0.80323, saving model to /content/drive/MyDrive/cross_attention/model.37-0.8032.h5\n",
            "296/296 [==============================] - 196s 663ms/step - loss: 0.2900 - masked_acc: 0.8032 - val_loss: 0.2976 - val_masked_acc: 0.8180\n",
            "Epoch 38/40\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2842 - masked_acc: 0.8061\n",
            "Epoch 38: masked_acc improved from 0.80323 to 0.80605, saving model to /content/drive/MyDrive/cross_attention/model.38-0.8061.h5\n",
            "296/296 [==============================] - 195s 658ms/step - loss: 0.2842 - masked_acc: 0.8061 - val_loss: 0.2938 - val_masked_acc: 0.8202\n",
            "Epoch 39/40\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2788 - masked_acc: 0.8084\n",
            "Epoch 39: masked_acc improved from 0.80605 to 0.80838, saving model to /content/drive/MyDrive/cross_attention/model.39-0.8084.h5\n",
            "296/296 [==============================] - 206s 696ms/step - loss: 0.2788 - masked_acc: 0.8084 - val_loss: 0.2898 - val_masked_acc: 0.8221\n",
            "Epoch 40/40\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2739 - masked_acc: 0.8111\n",
            "Epoch 40: masked_acc improved from 0.80838 to 0.81110, saving model to /content/drive/MyDrive/cross_attention/model.40-0.8111.h5\n",
            "296/296 [==============================] - 196s 661ms/step - loss: 0.2739 - masked_acc: 0.8111 - val_loss: 0.2861 - val_masked_acc: 0.8243\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f0c922c7ca0>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataloader, steps_per_epoch=train_steps, epochs=50, validation_data=val_dataloader, validation_steps=valid_steps,\\\n",
        "          callbacks=[modelsave_wt,tensorboard_callback],initial_epoch=40)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AcbY4FcLnUpK",
        "outputId": "8b955606-27d1-4484-813d-235cf8a2736e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 41/50\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2689 - masked_acc: 0.8138\n",
            "Epoch 41: masked_acc improved from 0.81110 to 0.81379, saving model to /content/drive/MyDrive/cross_attention/model.41-0.8138.h5\n",
            "296/296 [==============================] - 199s 667ms/step - loss: 0.2689 - masked_acc: 0.8138 - val_loss: 0.2834 - val_masked_acc: 0.8253\n",
            "Epoch 42/50\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2649 - masked_acc: 0.8155\n",
            "Epoch 42: masked_acc improved from 0.81379 to 0.81554, saving model to /content/drive/MyDrive/cross_attention/model.42-0.8155.h5\n",
            "296/296 [==============================] - 195s 658ms/step - loss: 0.2649 - masked_acc: 0.8155 - val_loss: 0.2801 - val_masked_acc: 0.8273\n",
            "Epoch 43/50\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2602 - masked_acc: 0.8180\n",
            "Epoch 43: masked_acc improved from 0.81554 to 0.81800, saving model to /content/drive/MyDrive/cross_attention/model.43-0.8180.h5\n",
            "296/296 [==============================] - 192s 650ms/step - loss: 0.2602 - masked_acc: 0.8180 - val_loss: 0.2771 - val_masked_acc: 0.8290\n",
            "Epoch 44/50\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2560 - masked_acc: 0.8202\n",
            "Epoch 44: masked_acc improved from 0.81800 to 0.82016, saving model to /content/drive/MyDrive/cross_attention/model.44-0.8202.h5\n",
            "296/296 [==============================] - 191s 647ms/step - loss: 0.2560 - masked_acc: 0.8202 - val_loss: 0.2738 - val_masked_acc: 0.8310\n",
            "Epoch 45/50\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2520 - masked_acc: 0.8224\n",
            "Epoch 45: masked_acc improved from 0.82016 to 0.82238, saving model to /content/drive/MyDrive/cross_attention/model.45-0.8224.h5\n",
            "296/296 [==============================] - 193s 652ms/step - loss: 0.2520 - masked_acc: 0.8224 - val_loss: 0.2719 - val_masked_acc: 0.8325\n",
            "Epoch 46/50\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2484 - masked_acc: 0.8242\n",
            "Epoch 46: masked_acc improved from 0.82238 to 0.82418, saving model to /content/drive/MyDrive/cross_attention/model.46-0.8242.h5\n",
            "296/296 [==============================] - 196s 661ms/step - loss: 0.2484 - masked_acc: 0.8242 - val_loss: 0.2689 - val_masked_acc: 0.8340\n",
            "Epoch 47/50\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2453 - masked_acc: 0.8256\n",
            "Epoch 47: masked_acc improved from 0.82418 to 0.82559, saving model to /content/drive/MyDrive/cross_attention/model.47-0.8256.h5\n",
            "296/296 [==============================] - 193s 649ms/step - loss: 0.2453 - masked_acc: 0.8256 - val_loss: 0.2674 - val_masked_acc: 0.8346\n",
            "Epoch 48/50\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2418 - masked_acc: 0.8276\n",
            "Epoch 48: masked_acc improved from 0.82559 to 0.82761, saving model to /content/drive/MyDrive/cross_attention/model.48-0.8276.h5\n",
            "296/296 [==============================] - 194s 654ms/step - loss: 0.2418 - masked_acc: 0.8276 - val_loss: 0.2646 - val_masked_acc: 0.8368\n",
            "Epoch 49/50\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2389 - masked_acc: 0.8293\n",
            "Epoch 49: masked_acc improved from 0.82761 to 0.82928, saving model to /content/drive/MyDrive/cross_attention/model.49-0.8293.h5\n",
            "296/296 [==============================] - 194s 655ms/step - loss: 0.2389 - masked_acc: 0.8293 - val_loss: 0.2627 - val_masked_acc: 0.8381\n",
            "Epoch 50/50\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2362 - masked_acc: 0.8307\n",
            "Epoch 50: masked_acc improved from 0.82928 to 0.83072, saving model to /content/drive/MyDrive/cross_attention/model.50-0.8307.h5\n",
            "296/296 [==============================] - 192s 650ms/step - loss: 0.2362 - masked_acc: 0.8307 - val_loss: 0.2601 - val_masked_acc: 0.8389\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f0caa8cf100>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataloader, steps_per_epoch=train_steps, epochs=60, validation_data=val_dataloader, validation_steps=valid_steps,\\\n",
        "          callbacks=[modelsave_wt,tensorboard_callback],initial_epoch=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uIS5VLddxqau",
        "outputId": "16ca3c4f-f1de-4a03-bee8-86a4f1c3a724"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 51/60\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2331 - masked_acc: 0.8322\n",
            "Epoch 51: masked_acc improved from 0.83072 to 0.83216, saving model to /content/drive/MyDrive/cross_attention/model.51-0.8322.h5\n",
            "296/296 [==============================] - 201s 670ms/step - loss: 0.2331 - masked_acc: 0.8322 - val_loss: 0.2590 - val_masked_acc: 0.8400\n",
            "Epoch 52/60\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2304 - masked_acc: 0.8339\n",
            "Epoch 52: masked_acc improved from 0.83216 to 0.83391, saving model to /content/drive/MyDrive/cross_attention/model.52-0.8339.h5\n",
            "296/296 [==============================] - 194s 657ms/step - loss: 0.2304 - masked_acc: 0.8339 - val_loss: 0.2566 - val_masked_acc: 0.8417\n",
            "Epoch 53/60\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2278 - masked_acc: 0.8351\n",
            "Epoch 53: masked_acc improved from 0.83391 to 0.83507, saving model to /content/drive/MyDrive/cross_attention/model.53-0.8351.h5\n",
            "296/296 [==============================] - 197s 666ms/step - loss: 0.2278 - masked_acc: 0.8351 - val_loss: 0.2554 - val_masked_acc: 0.8423\n",
            "Epoch 54/60\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2252 - masked_acc: 0.8365\n",
            "Epoch 54: masked_acc improved from 0.83507 to 0.83648, saving model to /content/drive/MyDrive/cross_attention/model.54-0.8365.h5\n",
            "296/296 [==============================] - 195s 658ms/step - loss: 0.2252 - masked_acc: 0.8365 - val_loss: 0.2537 - val_masked_acc: 0.8434\n",
            "Epoch 55/60\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2229 - masked_acc: 0.8377\n",
            "Epoch 55: masked_acc improved from 0.83648 to 0.83771, saving model to /content/drive/MyDrive/cross_attention/model.55-0.8377.h5\n",
            "296/296 [==============================] - 194s 654ms/step - loss: 0.2229 - masked_acc: 0.8377 - val_loss: 0.2529 - val_masked_acc: 0.8442\n",
            "Epoch 56/60\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2206 - masked_acc: 0.8390\n",
            "Epoch 56: masked_acc improved from 0.83771 to 0.83903, saving model to /content/drive/MyDrive/cross_attention/model.56-0.8390.h5\n",
            "296/296 [==============================] - 191s 646ms/step - loss: 0.2206 - masked_acc: 0.8390 - val_loss: 0.2505 - val_masked_acc: 0.8454\n",
            "Epoch 57/60\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2183 - masked_acc: 0.8400\n",
            "Epoch 57: masked_acc improved from 0.83903 to 0.83997, saving model to /content/drive/MyDrive/cross_attention/model.57-0.8400.h5\n",
            "296/296 [==============================] - 193s 651ms/step - loss: 0.2183 - masked_acc: 0.8400 - val_loss: 0.2500 - val_masked_acc: 0.8459\n",
            "Epoch 58/60\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2167 - masked_acc: 0.8413\n",
            "Epoch 58: masked_acc improved from 0.83997 to 0.84133, saving model to /content/drive/MyDrive/cross_attention/model.58-0.8413.h5\n",
            "296/296 [==============================] - 193s 650ms/step - loss: 0.2167 - masked_acc: 0.8413 - val_loss: 0.2482 - val_masked_acc: 0.8472\n",
            "Epoch 59/60\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2143 - masked_acc: 0.8424\n",
            "Epoch 59: masked_acc improved from 0.84133 to 0.84239, saving model to /content/drive/MyDrive/cross_attention/model.59-0.8424.h5\n",
            "296/296 [==============================] - 194s 654ms/step - loss: 0.2143 - masked_acc: 0.8424 - val_loss: 0.2470 - val_masked_acc: 0.8478\n",
            "Epoch 60/60\n",
            " 76/296 [======>.......................] - ETA: 2:16 - loss: 0.2113 - masked_acc: 0.8441"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataloader, steps_per_epoch=train_steps, epochs=65, validation_data=val_dataloader, validation_steps=valid_steps,\\\n",
        "          callbacks=[modelsave_wt,tensorboard_callback],initial_epoch=59)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1ZdRLy4fi4N",
        "outputId": "70d22014-98ac-46db-fe44-0738d8ac450e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 60/65\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2113 - masked_acc: 0.8438\n",
            "Epoch 60: masked_acc improved from 0.84217 to 0.84383, saving model to /content/drive/MyDrive/cross_attention/model.60-0.8438.h5\n",
            "296/296 [==============================] - 190s 636ms/step - loss: 0.2113 - masked_acc: 0.8438 - val_loss: 0.2349 - val_masked_acc: 0.8526\n",
            "Epoch 61/65\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2085 - masked_acc: 0.8453\n",
            "Epoch 61: masked_acc improved from 0.84383 to 0.84530, saving model to /content/drive/MyDrive/cross_attention/model.61-0.8453.h5\n",
            "296/296 [==============================] - 186s 628ms/step - loss: 0.2085 - masked_acc: 0.8453 - val_loss: 0.2329 - val_masked_acc: 0.8539\n",
            "Epoch 62/65\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2060 - masked_acc: 0.8466\n",
            "Epoch 62: masked_acc improved from 0.84530 to 0.84659, saving model to /content/drive/MyDrive/cross_attention/model.62-0.8466.h5\n",
            "296/296 [==============================] - 187s 633ms/step - loss: 0.2060 - masked_acc: 0.8466 - val_loss: 0.2320 - val_masked_acc: 0.8542\n",
            "Epoch 63/65\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2040 - masked_acc: 0.8477\n",
            "Epoch 63: masked_acc improved from 0.84659 to 0.84775, saving model to /content/drive/MyDrive/cross_attention/model.63-0.8477.h5\n",
            "296/296 [==============================] - 187s 633ms/step - loss: 0.2040 - masked_acc: 0.8477 - val_loss: 0.2302 - val_masked_acc: 0.8553\n",
            "Epoch 64/65\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.2016 - masked_acc: 0.8488\n",
            "Epoch 64: masked_acc improved from 0.84775 to 0.84884, saving model to /content/drive/MyDrive/cross_attention/model.64-0.8488.h5\n",
            "296/296 [==============================] - 188s 636ms/step - loss: 0.2016 - masked_acc: 0.8488 - val_loss: 0.2302 - val_masked_acc: 0.8557\n",
            "Epoch 65/65\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.1996 - masked_acc: 0.8502\n",
            "Epoch 65: masked_acc improved from 0.84884 to 0.85021, saving model to /content/drive/MyDrive/cross_attention/model.65-0.8502.h5\n",
            "296/296 [==============================] - 187s 632ms/step - loss: 0.1996 - masked_acc: 0.8502 - val_loss: 0.2278 - val_masked_acc: 0.8569\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7e4eccbb20>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataloader, steps_per_epoch=train_steps, epochs=70, validation_data=val_dataloader, validation_steps=valid_steps,\\\n",
        "          callbacks=[modelsave_wt,tensorboard_callback],initial_epoch=65)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vn5ON3p3nLtd",
        "outputId": "47592a7b-de78-45de-b169-2f74a16cacb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 66/70\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.1974 - masked_acc: 0.8510\n",
            "Epoch 66: masked_acc improved from 0.85021 to 0.85099, saving model to /content/drive/MyDrive/cross_attention/model.66-0.8510.h5\n",
            "296/296 [==============================] - 192s 640ms/step - loss: 0.1974 - masked_acc: 0.8510 - val_loss: 0.2268 - val_masked_acc: 0.8570\n",
            "Epoch 67/70\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.1952 - masked_acc: 0.8524\n",
            "Epoch 67: masked_acc improved from 0.85099 to 0.85240, saving model to /content/drive/MyDrive/cross_attention/model.67-0.8524.h5\n",
            "296/296 [==============================] - 188s 635ms/step - loss: 0.1952 - masked_acc: 0.8524 - val_loss: 0.2265 - val_masked_acc: 0.8577\n",
            "Epoch 68/70\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.1935 - masked_acc: 0.8533\n",
            "Epoch 68: masked_acc improved from 0.85240 to 0.85333, saving model to /content/drive/MyDrive/cross_attention/model.68-0.8533.h5\n",
            "296/296 [==============================] - 189s 640ms/step - loss: 0.1935 - masked_acc: 0.8533 - val_loss: 0.2249 - val_masked_acc: 0.8589\n",
            "Epoch 69/70\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.1915 - masked_acc: 0.8547\n",
            "Epoch 69: masked_acc improved from 0.85333 to 0.85469, saving model to /content/drive/MyDrive/cross_attention/model.69-0.8547.h5\n",
            "296/296 [==============================] - 190s 641ms/step - loss: 0.1915 - masked_acc: 0.8547 - val_loss: 0.2240 - val_masked_acc: 0.8597\n",
            "Epoch 70/70\n",
            "296/296 [==============================] - ETA: 0s - loss: 0.1899 - masked_acc: 0.8552\n",
            "Epoch 70: masked_acc improved from 0.85469 to 0.85520, saving model to /content/drive/MyDrive/cross_attention/model.70-0.8552.h5\n",
            "296/296 [==============================] - 191s 644ms/step - loss: 0.1899 - masked_acc: 0.8552 - val_loss: 0.2234 - val_masked_acc: 0.8599\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7e4ece4100>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prediction:"
      ],
      "metadata": {
        "id": "K1vJhIpUug9Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(input_sentence):\n",
        "  '''takes italian input sentence and outputs translated english sentence'''\n",
        "\n",
        "  encoder_seq = tknizer_ita.texts_to_sequences([input_sentence]) # tokenizing\n",
        "  encoder_seq = pad_sequences(encoder_seq, maxlen=20, dtype='int32', padding='post') #padding to len 20\n",
        "  encoder_output, encoder_h, encoder_c = model.encoder(encoder_seq) # encoding\n",
        "\n",
        "  decoder_input = tknizer_eng.texts_to_sequences(['<start>']) # feeding <start> token as 1st decoder input\n",
        "  decoder_state_h = encoder_h\n",
        "  decoder_state_c = encoder_c\n",
        "  prediction=[]\n",
        "  att_wt = []\n",
        "  for i in range(20): # iterating over full max_length\n",
        "    if i>0 and prediction[-1]==['<end>']: # end translation when <end> token predicted\n",
        "      break\n",
        "    else:\n",
        "      input=tf.expand_dims(decoder_input[0][-1],0)  \n",
        "      if i==0:\n",
        "       input=tf.expand_dims(input,0)\n",
        "      decoder_output, state_h,state_c,attention_weights, context_vector= model.layers[1].cross_attention(input, encoder_output,decoder_state_h,decoder_state_c)\n",
        "      att_wt.append(attention_weights)\n",
        "      index = tf.argmax(decoder_output,axis=-1).numpy()\n",
        "      prediction.append(tknizer_eng.sequences_to_texts([index]))\n",
        "      decoder_input[0].append(index)\n",
        "      decoder_state_h = state_h\n",
        "      decoder_state_c = state_c\n",
        "    \n",
        "  predicted_sent= prediction[0][0]\n",
        "  for word in prediction:\n",
        "    predicted_sent = predicted_sent + ' ' + word[0]\n",
        "  return predicted_sent,att_wt"
      ],
      "metadata": {
        "id": "TWgja-m6i3IU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# predicting on random unseen 5 italian sentences\n",
        "\n",
        "for doc in test.sample(5).values:\n",
        "  sent=doc[0]\n",
        "  eng_sent = doc[-1]\n",
        "  print('italian: ',sent)\n",
        "  print('English True: ',eng_sent)\n",
        "  trans,_ = predict(sent)\n",
        "  print('Model Translation: ',trans,'\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c5a7e1c-b708-4990-d82f-66bd41cef5c9",
        "id": "zVvkF0RBi3IV"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "italian:  vedo cosavete fatto lì\n",
            "English True:  i see what you did there <end>\n",
            "Model Translation:  i i see what you have done there <end> \n",
            "\n",
            "italian:  tom non è un fisico\n",
            "English True:  tom is not a physician <end>\n",
            "Model Translation:  tom tom is not a physician <end> \n",
            "\n",
            "italian:  cè un costo di consegna\n",
            "English True:  is there a delivery charge <end>\n",
            "Model Translation:  there there is a charge of the delivery <end> \n",
            "\n",
            "italian:  è un tizio strano\n",
            "English True:  he is a strange guy <end>\n",
            "Model Translation:  it it is a strange guy <end> \n",
            "\n",
            "italian:  tutti qua sanno che non mangiamo la carne di maiale\n",
            "English True:  everyone here knows that we do not eat pork <end>\n",
            "Model Translation:  everyone everyone here knows we do not eat pork <end> \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bleu_score on Test Dataset:"
      ],
      "metadata": {
        "id": "gRVWaNIaxW1T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk.translate.bleu_score as bleu"
      ],
      "metadata": {
        "id": "xpJs8vK9i3IX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def bleu_score(input,weights):\n",
        "  bleu_score=[]\n",
        "  for doc in input:\n",
        "    input_sentence = doc[0]\n",
        "    eng_sent = doc[-1]\n",
        "    predicted_sent,_ = predict(input_sentence)\n",
        "    bleu_score.append(bleu.sentence_bleu(eng_sent, predicted_sent,weights=weights))\n",
        "  return np.mean(bleu_score)"
      ],
      "metadata": {
        "id": "byxKkWbFi3KW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# bleu score by only matching uni-grams\n",
        "\n",
        "bleu_score_test = bleu_score( input = test.values, weights = (1,0,0,0) )\n",
        "print('average test data bleu score: ',bleu_score_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "524451ae-fb0b-4bf9-c7cb-a5e3afa88607",
        "id": "TAYTqLwYi3KY"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average test data bleu score:  0.4451662890214658\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# bleu score by matching 1 to 4-grams\n",
        "\n",
        "bleu_score_test = bleu_score( input = test.values, weights = (0.25,0.25,0.25,0.25) )\n",
        "print('average test data cumulative 4-gram bleu score: ',bleu_score_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cCiIpbHRtbdn",
        "outputId": "f2fd6ff8-129c-4b0b-c18e-e4d2070513a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average test data cumulative 4-gram bleu score:  1.479362713798278e-231\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}