lectures/cap15_3_seq2seq_machine_translation.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/viniciusrpb/cic0269_natural_language_processing/blob/main/lectures/cap15_3_seq2seq_machine_translation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Capítulo 15 - Arquiteturas Encoder-Decoder\n",
        "\n",
        "## Modelos *Sequence to Sequence* (seq2seq)\n",
        "\n",
        "Este notebook é fortemente baseado no tutorial disponibilizado no [Keras Blog](https://keras.io/examples/nlp/lstm_seq2seq/) por Francois Chollet.\n",
        "\n",
        "## Machine Translation"
      ],
      "metadata": {
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      }
    },
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        "![machinetranslation.drawio.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "gmJvA8GpK3zK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "G7kcBkVwr1sI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow-datasets"
      ],
      "metadata": {
        "id": "L3GftSsEJtvn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_datasets as tfds\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from keras.utils.np_utils import to_categorical\n",
        "from keras.models import Sequential,Model\n",
        "from keras.layers import Dense,Embedding,Bidirectional,LSTM,Activation,Input,Dropout,BatchNormalization,TimeDistributed"
      ],
      "metadata": {
        "id": "-9-TJXHBK8nD"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds_train = tfds.load('ted_hrlr_translate/pt_to_en', split='train',\n",
        "                     shuffle_files=True,\n",
        "                     with_info=True,\n",
        "                     as_supervised=True)\n",
        "\n",
        "ds_valid = tfds.load('ted_hrlr_translate/pt_to_en', split='validation',\n",
        "                     shuffle_files=False,\n",
        "                     with_info=True,\n",
        "                     as_supervised=True)\n",
        "\n",
        "ds_test = tfds.load('ted_hrlr_translate/pt_to_en', split='test',\n",
        "                    shuffle_files=False,\n",
        "                    with_info=True,\n",
        "                    as_supervised=True)"
      ],
      "metadata": {
        "id": "vYUZJSNPJ52-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148,
          "referenced_widgets": [
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        },
        "outputId": "6128cd86-477b-4f42-c95b-8709b9a87de4"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 124.94 MiB (download: 124.94 MiB, generated: Unknown size, total: 124.94 MiB) to ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "68a118f48dac44beb85fbd68cec29ff2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f31b2123d19b4365b56ef15e80390060"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a8f1ca2e8ea2413f91c2c8e05bf4d25d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e3d5dd4fb4e4fab8e56e5c5273be361"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...:   0%|          | 0/51785 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "57e358574d284ccf9530017b43f740eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteULHT9Z/ted_hrlr_translate-train.tf…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b56b4f7138847b28c98bec3fcb60c52"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation examples...:   0%|          | 0/1193 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01f75f9b55ef41a2a57e729ce1f534f0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteULHT9Z/ted_hrlr_translate-validati…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "35d27a08edb74b6fa7be50ef9d19ac54"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test examples...:   0%|          | 0/1803 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f03600b73e5144f2bb224dde0c44a7eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0.incompleteULHT9Z/ted_hrlr_translate-test.tfr…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset ted_hrlr_translate downloaded and prepared to ~/tensorflow_datasets/ted_hrlr_translate/pt_to_en/1.0.0. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def ds2DataFrame(ds):\n",
        "    \n",
        "    dic = {}\n",
        "    dic['pt'] = []\n",
        "    dic['en'] = []\n",
        "    for elem in ds[0]:\n",
        "        port = elem[0].numpy().decode('utf-8')\n",
        "        eng = elem[1].numpy().decode('utf-8')\n",
        "        dic['pt'].append(port)\n",
        "        dic['en'].append(eng)\n",
        "\n",
        "    return pd.DataFrame.from_dict(dic)"
      ],
      "metadata": {
        "id": "XXYCIjKbjoMf"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = ds2DataFrame(ds_train)\n",
        "df_valid = ds2DataFrame(ds_valid)\n",
        "df_test = ds2DataFrame(ds_test)"
      ],
      "metadata": {
        "id": "47EFzFoeKO6G"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train"
      ],
      "metadata": {
        "id": "4NlErqdoLOu6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "ecc214ab-c50e-4982-c9f3-06179d35957b"
      },
      "execution_count": 6,
      "outputs": [
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              "0      e quando melhoramos a procura , tiramos a únic...   \n",
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              "51783                now , the caterpillar did n't die .  \n",
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_tokens_encoder = 32\n",
        "num_tokens_decoder = 32\n",
        "num_neurons = 128\n",
        "max_encoder_seq_length = 32\n",
        "max_decoder_seq_length = 32"
      ],
      "metadata": {
        "id": "mQqpbVARMz9Z"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Criar o dicionário para as palavras em português"
      ],
      "metadata": {
        "id": "9YlIK_K-DZD-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_dictionary(df):\n",
        "\n",
        "    word2index = {}\n",
        "    word2index['<OOV>'] = 0\n",
        "    word2index['<BEG>'] = 1\n",
        "    word2index['<END>'] = 2\n",
        "\n",
        "    ind = 3\n",
        "    for text in df:\n",
        "        for word in text.split():\n",
        "            if word not in word2index:\n",
        "                word2index[word] = ind\n",
        "                ind+=1\n",
        "\n",
        "    index2word = {}\n",
        "    for key in word2index:\n",
        "        value = word2index[key]\n",
        "        index2word[value] = key\n",
        "\n",
        "    return word2index,index2word"
      ],
      "metadata": {
        "id": "5qGfsE7WDYjW"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_pt,index_pt = create_dictionary(df_train['pt'])\n",
        "vocab_en,index_en = create_dictionary(df_train['en'])\n",
        "\n",
        "vocab_pt_size = len(vocab_pt)\n",
        "vocab_en_size = len(vocab_en)\n",
        "\n",
        "tokenizer_pt = Tokenizer(num_words = vocab_pt_size)\n",
        "tokenizer_pt.fit_on_texts(df_train['pt'])\n",
        "\n",
        "tokenizer_en = Tokenizer(num_words = vocab_en_size)\n",
        "tokenizer_en.fit_on_texts(df_train['en'])"
      ],
      "metadata": {
        "id": "H8FfC5YrJH46"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_data(df):\n",
        "\n",
        "    X = []\n",
        "    y = []\n",
        "\n",
        "    limit = 0\n",
        "\n",
        "    for sentence in df:\n",
        "\n",
        "        if limit > 5000:\n",
        "            break\n",
        "\n",
        "        tokens = sentence.split()\n",
        "        lista_x = ['<BEG>']\n",
        "        lista_y = []\n",
        "        for i in range(0,len(tokens)):\n",
        "\n",
        "            lista_y.append(tokens[i])\n",
        "            lista_x.append(tokens[i])\n",
        "\n",
        "        lista_y.append('<END>')\n",
        "\n",
        "        X.append(lista_x)\n",
        "        y.append(lista_y)\n",
        "\n",
        "        limit+=1\n",
        "    \n",
        "    return X,y"
      ],
      "metadata": {
        "id": "c9SC-RUpLbmE"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_pt,y_train_pt = prepare_data(df_train['pt'])\n",
        "#X_valid_pt,y_valid_pt = prepare_data(df_train['pt'])\n",
        "\n",
        "X_train_en,y_train_en = prepare_data(df_train['en'])\n",
        "#X_valid_en,y_valid_en = prepare_data(df_train['en'])"
      ],
      "metadata": {
        "id": "6h72-qkqPwHw"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def text2sequences(data,vocab):\n",
        "    corpus_int = []\n",
        "    for instance in data:\n",
        "        instance_int = []\n",
        "        for term in instance:\n",
        "            if term in vocab:\n",
        "                instance_int.append(vocab[term])\n",
        "            else:\n",
        "                instance_int.append(vocab['<OOV>'])\n",
        "        corpus_int.append(instance_int)\n",
        "    \n",
        "    return np.array(corpus_int)"
      ],
      "metadata": {
        "id": "K5ExQkcjkErG"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_pt_sequences = text2sequences(X_train_pt,vocab_pt)\n",
        "#valid_pt_sequences = text2sequences(X_valid_pt,vocab_pt)\n",
        "#test_pt_sequences = text2sequences(X_test_pt,vocab_pt)\n",
        "\n",
        "train_en_sequences = text2sequences(X_train_en,vocab_en)\n",
        "#valid_en_sequences = text2sequences(X_valid_en,vocab_en)\n",
        "\n",
        "y_train_pt_sequences = text2sequences(y_train_pt,vocab_pt)\n",
        "y_train_en_sequences = text2sequences(y_train_en,vocab_en)"
      ],
      "metadata": {
        "id": "vxX93eWbG7Yg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec8385a5-1350-48dd-cce8-66ac5456e090"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-46fe4ebd28de>:12: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return np.array(corpus_int)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_pt_sequences[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fhvorfi4WlK",
        "outputId": "76c8f57c-90d5-49b4-8147-574d5732b5ba"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 18, 3, 19, 20, 21, 22, 23, 24]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_pt_sequences[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7PMRX00m5SGW",
        "outputId": "4d24099f-96e8-4b82-fef2-12286320fd2b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[18, 3, 19, 20, 21, 22, 23, 24, 2]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trunc_type = 'post'\n",
        "padding_type = 'post'\n",
        "max_length = 32\n",
        "\n",
        "train_pt_padded = pad_sequences(train_pt_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "#valid_pt_padded = pad_sequences(valid_pt_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "train_en_padded = pad_sequences(train_en_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "#valid_en_padded = pad_sequences(valid_en_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "#test_padded = pad_sequences(test_en_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)"
      ],
      "metadata": {
        "id": "YIBQ7daflyDg"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_pt_padded = np.array(pad_sequences(y_train_pt_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type))\n",
        "\n",
        "y_train_en_padded = np.array(pad_sequences(y_train_en_sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type))"
      ],
      "metadata": {
        "id": "5jxz-ZC51j9d"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_en_padded[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZ9tZP8m5W4m",
        "outputId": "75520470-d744-4fa4-aba7-93ab6b6c4754"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([21, 22, 23, 24, 25, 26, 27,  2,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
              "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "      dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_pt_padded[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hMW4FMSd5a2u",
        "outputId": "e0104de5-998e-48be-a9d6-efd70e0246cb"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([18,  3, 19, 20, 21, 22, 23, 24,  2,  0,  0,  0,  0,  0,  0,  0,  0,\n",
              "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "      dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_pt_padded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVS2_sco5oxu",
        "outputId": "b84bab07-3f3e-4281-fe5d-53620ccdc5c5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  1,   3,   4, ...,   0,   0,   0],\n",
              "       [  1,  18,   3, ...,   0,   0,   0],\n",
              "       [  1,  18,  25, ...,   0,   0,   0],\n",
              "       ...,\n",
              "       [  1,  45,  68, ...,  46,   0,   0],\n",
              "       [  1, 833,   8, ...,   0,   0,   0],\n",
              "       [  1,  45, 852, ...,   0,   0,   0]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   ```state_h``` é a saída da LSTM após processar a última palavra;\n",
        "\n",
        "Observe que ```encoder_outputs``` e ```state_h``` serão idênticos quando ```return_sequences``` é Falso.\n",
        "\n",
        "\n",
        "Assim, ```state_h``` e ```state_c``` compões o context vector:\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ZspK4j1Px-B7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoder_inputs = Input(shape=(None,num_tokens_encoder))\n",
        "encoder = LSTM(num_neurons, return_state=True)\n",
        "encoder_outputs, state_h, state_c = encoder(encoder_inputs)\n",
        "encoder_states = [state_h, state_c]"
      ],
      "metadata": {
        "id": "qaocG-c_kzT8"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "decoder_inputs = Input(shape=(None, num_tokens_decoder))\n",
        "decoder_lstm = LSTM(num_neurons, return_sequences=True, return_state=True)\n",
        "decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)\n",
        "decoder_dense = Dense(vocab_en_size+3, activation='softmax')\n",
        "decoder_outputs = decoder_dense(decoder_outputs)"
      ],
      "metadata": {
        "id": "mYXVp9r-t4Ct"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos utilizar a classe [Model](https://keras.io/api/models/model/) para construir o modelo completo"
      ],
      "metadata": {
        "id": "q8IcsIjzt9VD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Model(inputs=[encoder_inputs, decoder_inputs], outputs=decoder_outputs)"
      ],
      "metadata": {
        "id": "N52XP7bQt8Pj"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tji0-xkX6dBb",
        "outputId": "9ae87219-fe54-4aa1-fcd1-508347abf447"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, None, 32)]   0           []                               \n",
            "                                                                                                  \n",
            " input_2 (InputLayer)           [(None, None, 32)]   0           []                               \n",
            "                                                                                                  \n",
            " lstm (LSTM)                    [(None, 128),        82432       ['input_1[0][0]']                \n",
            "                                 (None, 128),                                                     \n",
            "                                 (None, 128)]                                                     \n",
            "                                                                                                  \n",
            " lstm_1 (LSTM)                  [(None, None, 128),  82432       ['input_2[0][0]',                \n",
            "                                 (None, 128),                     'lstm[0][1]',                   \n",
            "                                 (None, 128)]                     'lstm[0][2]']                   \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, None, 28856)  3722424     ['lstm_1[0][0]']                 \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 3,887,288\n",
            "Trainable params: 3,887,288\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "\n",
        "history = model.fit([train_pt_padded, train_en_padded],[y_train_en_padded],batch_size=32,epochs=5)"
      ],
      "metadata": {
        "id": "lymze1enCk1r"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}