Copy_of_[Blanks]_Project_Machine_Translation_(ENG_FR).ipynb

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      "source": [
        "##Machine Translation Using a Seq2Seq Architecture\n",
        "© 2023, Zaka AI, Inc. All Rights Reserved.\n",
        "\n",
        "---\n",
        "The goal of this colab is to get you more familiar with the Seq2Seq models and their challenges. For this reason, you will be working on machine translation problem where we would have a sentence as input (in english), and the output is gonna be the translated sentence (in french). So just like what happens with Google Translate.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TeK4LPupvg_c"
      },
      "source": [
        "**Just to give you a heads up:** We won't be having a model performing like Google translate, but at least we will have an idea about how Google Translate works and the challenges that exist with a translation problem.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SBTvDTzBv293"
      },
      "source": [
        "## Importing Libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_j1ZzS3v6N3"
      },
      "source": [
        "We start by importing numpy and pandas and then we can add the rest"
      ]
    },
    {
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      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "n0IARXAX1e1m",
        "outputId": "66a3bff4-04c1-4928-db3b-22735f805b7f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to\n",
            "[nltk_data]     C:\\Users\\Nasam\\AppData\\Roaming\\nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.layers import Input, LSTM, Embedding, Dense, Bidirectional, Concatenate\n",
        "from tensorflow.keras.models import Model\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAcLqZ7uv-SJ"
      },
      "source": [
        "We clone the github repository where our data exists. Here is the github link: https://github.com/zaka-ai/machine_learning_certification/tree/main/Challenge%207 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i3hLN42axOjn"
      },
      "source": [
        "## Getting the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "0-M7cFxTPpqy",
        "outputId": "53df8420-f7c0-40a8-b43e-b8b09240d47c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "^C\n",
            "c:\\Users\\Nasam\\Downloads\\machine_learning_certification\n"
          ]
        }
      ],
      "source": [
        "#Test Your Zaka\n",
        "!git clone https://github.com/zaka-ai/machine_learning_certification.git\n",
        "\n",
        "%cd machine_learning_certification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BaPr0N8cwGAv"
      },
      "source": [
        "We read the english sentences in a dataframe named \"english\", and the french sentences in a dataframe named \"french\""
      ]
    },
    {
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            "text/plain": [
              "                                        en_sentences\n",
              "0  new jersey is sometimes quiet during autumn , ...\n",
              "1  the united states is usually chilly during jul...\n",
              "2  california is usually quiet during march , and...\n",
              "3  the united states is sometimes mild during jun...\n",
              "4  your least liked fruit is the grape , but my l..."
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Test Your Zaka (English)\n",
        "english = pd.read_csv('/content/machine_learning_certification/Challenge 7/en.csv', names=['en_sentences'], header=None)\n",
        "english.head()"
      ]
    },
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              "      <th>0</th>\n",
              "      <td>new jersey est parfois calme pendant l' automn...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>california est généralement calme en mars , et...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>les états-unis est parfois légère en juin , et...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "                                                     [key], {});\n",
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            "text/plain": [
              "                                        fr_sentences\n",
              "0  new jersey est parfois calme pendant l' automn...\n",
              "1  les états-unis est généralement froid en juill...\n",
              "2  california est généralement calme en mars , et...\n",
              "3  les états-unis est parfois légère en juin , et...\n",
              "4  votre moins aimé fruit est le raisin , mais mo..."
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Test Your Zaka (French)\n",
        "french = pd.read_csv('/content/machine_learning_certification/Challenge 7/fr.csv', names=['fr_sentences'], header=None)\n",
        "french.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jr8OO1OhwSp4"
      },
      "source": [
        "**How many sentences does each of the files contain?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhWJP-b02HKq",
        "outputId": "5662f895-2226-401c-c9c4-21f97c5c2a1e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of english sentences: 137860\n",
            "number of french sentences: 137860\n"
          ]
        }
      ],
      "source": [
        "#Test Your Zaka\n",
        "print(\"number of english sentences:\", english.shape[0])\n",
        "print('number of french sentences:', french.shape[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITGJN5tIwkDO"
      },
      "source": [
        "Now let us concatenate the 2 dataframes into one dataframe that we call **df** where one column has the english senetnces and the other has the french sentences"
      ]
    },
    {
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              "      <th>2</th>\n",
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              "      <td>california est généralement calme en mars , et...</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
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              "      <td>les états-unis est parfois légère en juin , et...</td>\n",
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              "          const element = document.querySelector('#df-da60db75-46b4-477e-906d-0291387d1f71');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ],
            "text/plain": [
              "                                        en_sentences  \\\n",
              "0  new jersey is sometimes quiet during autumn , ...   \n",
              "1  the united states is usually chilly during jul...   \n",
              "2  california is usually quiet during march , and...   \n",
              "3  the united states is sometimes mild during jun...   \n",
              "4  your least liked fruit is the grape , but my l...   \n",
              "\n",
              "                                        fr_sentences  \n",
              "0  new jersey est parfois calme pendant l' automn...  \n",
              "1  les états-unis est généralement froid en juill...  \n",
              "2  california est généralement calme en mars , et...  \n",
              "3  les états-unis est parfois légère en juin , et...  \n",
              "4  votre moins aimé fruit est le raisin , mais mo...  "
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Test Your Zaka\n",
        "df = pd.concat([english, french], axis = 1)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nAr_caXkwwE7"
      },
      "source": [
        "Let's name the columns as **English** and **French** so that we access them easier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "eOHiQDXx3jFS",
        "outputId": "21aaf067-1b04-4751-8b51-68deaf4a32b9"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>English</th>\n",
              "      <th>French</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>2</th>\n",
              "      <td>california is usually quiet during march , and...</td>\n",
              "      <td>california est généralement calme en mars , et...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>the united states is sometimes mild during jun...</td>\n",
              "      <td>les états-unis est parfois légère en juin , et...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>your least liked fruit is the grape , but my l...</td>\n",
              "      <td>votre moins aimé fruit est le raisin , mais mo...</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "                                             English  \\\n",
              "0  new jersey is sometimes quiet during autumn , ...   \n",
              "1  the united states is usually chilly during jul...   \n",
              "2  california is usually quiet during march , and...   \n",
              "3  the united states is sometimes mild during jun...   \n",
              "4  your least liked fruit is the grape , but my l...   \n",
              "\n",
              "                                              French  \n",
              "0  new jersey est parfois calme pendant l' automn...  \n",
              "1  les états-unis est généralement froid en juill...  \n",
              "2  california est généralement calme en mars , et...  \n",
              "3  les états-unis est parfois légère en juin , et...  \n",
              "4  votre moins aimé fruit est le raisin , mais mo...  "
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Test Your Zaka\n",
        "df.rename(columns={'en_sentences':'English', 'fr_sentences':'French'}, inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4xc1TsEHw9yC"
      },
      "source": [
        "Pick a sentence and print it in both languages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QuRVWch23ujo",
        "outputId": "58164150-d194-4d3a-d4b6-157ee02fc688"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "the united states is sometimes mild during june , and it is cold in september .\n",
            "les états-unis est parfois légère en juin , et il fait froid en septembre .\n"
          ]
        }
      ],
      "source": [
        "#Test Your Zaka\n",
        "print(df.loc[3, 'English'])\n",
        "print(df.loc[3, 'French'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FQjXYP1txFCi"
      },
      "source": [
        "##Cleaning Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xgz6jIoVxHUF"
      },
      "source": [
        "The data that we have is almost clean as we can see, we just need to remove the punctuations inside of it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6YYOt5QftcFI"
      },
      "outputs": [],
      "source": [
        "#Test Your Zaka\n",
        "def remove_punctuation(text):\n",
        "    translator = str.maketrans('', '', \"!\\\"#$%&'()*+,-./:;<=>?@[\\]^_{|}~\")\n",
        "    return text.translate(translator)\n",
        "\n",
        "df_clean = df.applymap(remove_punctuation)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0C1qsC9LxZPb"
      },
      "source": [
        "Make sure that the punctuation is removed by printing the example that you printed earlier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T80tiWxe84G7",
        "outputId": "d6b93c28-523c-48a9-a94e-0fc62472d04c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "the united states is sometimes mild during june  and it is cold in september \n",
            "les étatsunis est parfois légère en juin  et il fait froid en septembre \n"
          ]
        }
      ],
      "source": [
        "#Test Your Zaka\n",
        "print(df_clean.loc[3, 'English'])\n",
        "print(df_clean.loc[3, 'French'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZuFNjoBAx4oN"
      },
      "source": [
        "##Exploring the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATfefzPExi2k"
      },
      "source": [
        "Add a column **ENG Length** to the dataset that shows how many words does a sentence contain, and do the same for french in a column called **FR Length**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dakeo81s352S"
      },
      "outputs": [],
      "source": [
        "#Test Your Zaka\n",
        "df_clean['ENG Length'] = df_clean['English'].str.strip().apply(lambda x: len(x.split()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "49keasjaPaaK"
      },
      "outputs": [],
      "source": [
        "#Test Your Zaka\n",
        "df_clean['FR Length'] = df_clean['French'].str.strip().apply(lambda x: len(x.split()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AjQLW0K5xwx1"
      },
      "source": [
        "Visualize the distribution of the lengths of english sentences and french sentences."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "_q_UIMJ09L24",
        "outputId": "eccce892-f07a-4989-ed2f-14d114101808"
      },
      "outputs": [
        {
          "data": {
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IxpJEIiKisSSRiIhoLEkkIiIaSxKJiIjG2nkV34iIfk04+rIh3+bCkz8w5NtcW6UnEhERjSWJREREYxnOij5lqCEi+pKeSERENJYkEhERjSWJREREY0kiERHRWNuSiKRtJF0t6Q5J8yUdVeLHS1os6eby2Ke2zjGSFki6S9JetfiUElsg6ehafFtJN5T4jySt3679iYiIl2tnT2Ql8HnbOwC7AkdI2qEsO832pPKYDVCWHQC8CZgCfFvSKEmjgDOAvYEdgANr9ZxS6no98BhwSBv3JyIiumlbErG9xPavy/STwJ3A2D5WmQpcaPs52/cCC4Cdy2OB7XtsPw9cCEyVJGB34KKy/rnAvm3ZmYiI6NGQHBORNAF4C3BDCR0p6VZJMyRtXmJjgQdqqy0qsd7iWwKP217ZLd7T9qdLmidp3rJlywZjlyIigiFIIpJeAVwMfM72CuBMYDtgErAEOLXdbbB9lu3JtiePGTOm3ZuLiBgx2vqLdUnrUSWQH9j+TwDbD9eWfxf4SZldDGxTW31cidFL/FFgM0nrlt5IvXxERAyBdp6dJeBs4E7b36rFt64V+zBwe5meBRwgaQNJ2wITgRuBucDEcibW+lQH32fZNnA1sF9Zfxpwabv2JyIiXq6dPZF3Ah8HbpN0c4l9iersqkmAgYXApwBsz5c0E7iD6syuI2y/ACDpSOAKYBQww/b8Ut8XgQslfQ34DVXSioiIIdK2JGL7WkA9LJrdxzonASf1EJ/d03q276E6eysiIjogv1iPiIjGkkQiIqKxJJGIiGgsSSQiIhpLEomIiMaSRCIiorGWkoik/9XuhkRExPDTak/k25JulHS4pFe1tUURETFstJREbL8b+BjVNaxukvRDSXu0tWUREbHGa/mYiO27gWOpLjXyHuB0Sb+V9FftalxERKzZWj0m8r8lnUZ1Y6ndgQ/afmOZPq2N7YuIiDVYq9fO+lfge8CXbD/bFbT9oKRj29KyiIhY47WaRD4APFu7qu46wIa2n7F9fttaFxERa7RWj4n8DNioNr9xiUVExAjWahLZ0PZTXTNleuP2NCkiIoaLVpPI05J26pqR9Fbg2T7KR0TECNDqMZHPAf8h6UGqG039GfDRdjUqIiKGh5aSiO25kt4AbF9Cd9n+Y/uaFRERw8FAbo/7NmBCWWcnSdg+ry2tioiIYaGlJCLpfGA74GbghRI2kCQSETGCtXpgfTLwTtuH2/5MeXy2rxUkbSPpakl3SJov6agS30LSHEl3l7+bl7gknS5pgaRbux3In1bK3y1pWi3+Vkm3lXVOl6SBPwUREdFUq0nkdqqD6QOxEvi87R2AXYEjJO0AHA1cZXsicFWZB9gbmFge04EzoUo6wHHALsDOwHFdiaeUObS23pQBtjEiIlZDq8dERgN3SLoReK4raPtDva1gewmwpEw/KelOYCwwFditFDsXuIbqoo5TgfNsG7he0maSti5l59heDiBpDjBF0jXApravL/HzgH2By1vcp4iIWE2tJpHjV2cjkiYAbwFuALYqCQbgIWCrMj0WeKC22qIS6yu+qId4T9ufTtW7Yfz48auxJxERUdfq/UR+ASwE1ivTc4Fft7KupFcAFwOfs72iW72mOkDfVrbPsj3Z9uQxY8a0e3MRESNGq5eCPxS4CPhOCY0F/quF9dajSiA/sP2fJfxwGaai/F1a4oupbnrVZVyJ9RUf10M8IiKGSKsH1o8A3gmsgJduUPXqvlYoZ0qdDdxp+1u1RbOArjOspgGX1uIHlbO0dgWeKMNeVwB7Stq8HFDfE7iiLFshadeyrYNqdUVExBBo9ZjIc7af7zqDVtK69D8M9U7g48Btkm4usS8BJwMzJR0C3Ad8pCybDewDLACeAQ4GsL1c0olUQ2gAJ3QdZAcOB86husLw5eSgekTEkGo1ifxC0peAjcq91Q8HftzXCravpbrOVk/e10N5U/V4eqprBjCjh/g8YMe+mx4REe3S6nDW0cAy4DbgU1S9htzRMCJihGv1AowvAt8tj4iICKD1a2fdSw/HQGy/btBbFBERw0arx0Qm16Y3BPYHthj85kRExHDS6o8NH609Ftv+Z+AD7W1aRESs6VodztqpNrsOVc9kIPciiYiItVCrieDU2vRKqkugfKTnohERMVK0enbWe9vdkIiIGH5aHc76+76Wd7usSUREjBADOTvrbVTXtwL4IHAjcHc7GhUREcNDq0lkHLCT7ScBJB0PXGb7b9rVsIiIWPO1etmTrYDna/PP86ebSUVExAjVak/kPOBGSZeU+X2pbm0bEREjWKtnZ50k6XLg3SV0sO3ftK9ZERExHLQ6nAWwMbDC9r8AiyRt26Y2RUTEMNHq7XGPA74IHFNC6wHfb1ejIiJieGi1J/Jh4EPA0wC2HwRe2a5GRUTE8NBqEnm+3HnQAJI2aV+TIiJiuGg1icyU9B1gM0mHAj8jN6iKiBjx+k0ikgT8CLgIuBjYHviK7X/tZ70ZkpZKur0WO17SYkk3l8c+tWXHSFog6S5Je9XiU0psgaSja/FtJd1Q4j+StP6A9jwiIlZbv0mkDGPNtj3H9j/Y/oLtOS3UfQ4wpYf4abYnlcdsAEk7AAcAbyrrfFvSKEmjgDOAvYEdgANLWYBTSl2vBx4DDmmhTRERMYhaHc76taS3DaRi278ElrdYfCpwoe3nbN8LLAB2Lo8Ftu+x/TxwITC19I52p+odQfXDx30H0r6IiFh9rSaRXYDrJf1e0q2SbpN0a8NtHlnqmCFp8xIbCzxQK7OoxHqLbwk8bntlt3hERAyhPn+xLmm87fuBvfoqNwBnAidSneV1ItXNrj45SHX3StJ0YDrA+PHj2725iIgRo7+eyH8B2L4P+Jbt++qPgW7M9sO2X7D9ItXZXTuXRYuBbWpFx5VYb/FHqc4UW7dbvLftnmV7su3JY8aMGWizIyKiF/0lEdWmX7e6G5O0dW32w0DXmVuzgAMkbVAupzKR6n4lc4GJ5Uys9akOvs8qB/uvBvYr608DLl3d9kVExMD0dwFG9zLdL0kXALsBoyUtAo4DdpM0qdS1EPgUgO35kmYCd1Ddw/0I2y+Ueo4ErgBGATNszy+b+CJwoaSvAb8Bzh5I+yIiYvX1l0TeLGkFVY9kozJNmbftTXtb0faBPYR7/aC3fRJwUg/x2cDsHuL38KfhsIiI6IA+k4jtUUPVkIiIGH4Gcin4iIiIVSSJREREY0kiERHRWJJIREQ0liQSERGNJYlERERjSSIREdFYkkhERDSWJBIREY0liURERGNJIhER0ViSSERENJYkEhERjSWJREREY0kiERHRWJJIREQ0liQSERGNJYlERERjSSIREdFY25KIpBmSlkq6vRbbQtIcSXeXv5uXuCSdLmmBpFsl7VRbZ1opf7ekabX4WyXdVtY5XZLatS8REdGzdvZEzgGmdIsdDVxleyJwVZkH2BuYWB7TgTOhSjrAccAuwM7AcV2Jp5Q5tLZe921FRESbtS2J2P4lsLxbeCpwbpk+F9i3Fj/PleuBzSRtDewFzLG93PZjwBxgSlm2qe3rbRs4r1ZXREQMkaE+JrKV7SVl+iFgqzI9FnigVm5RifUVX9RDvEeSpkuaJ2nesmXLVm8PIiLiJR07sF56EB6ibZ1le7LtyWPGjBmKTUZEjAhDnUQeLkNRlL9LS3wxsE2t3LgS6ys+rod4REQMoaFOIrOArjOspgGX1uIHlbO0dgWeKMNeVwB7Stq8HFDfE7iiLFshaddyVtZBtboiImKIrNuuiiVdAOwGjJa0iOosq5OBmZIOAe4DPlKKzwb2ARYAzwAHA9heLulEYG4pd4LtroP1h1OdAbYRcHl5RETEEGpbErF9YC+L3tdDWQNH9FLPDGBGD/F5wI6r08aIiFg9+cV6REQ0liQSERGNJYlERERjSSIREdFYkkhERDSWJBIREY0liURERGNJIhER0ViSSERENJYkEhERjSWJREREY0kiERHRWJJIREQ0liQSERGNJYlERERjSSIREdFYkkhERDSWJBIREY0liURERGNJIhER0VhHkoikhZJuk3SzpHkltoWkOZLuLn83L3FJOl3SAkm3StqpVs+0Uv5uSdM6sS8RESNZJ3si77U9yfbkMn80cJXticBVZR5gb2BieUwHzoQq6QDHAbsAOwPHdSWeiIgYGut2ugE1U4HdyvS5wDXAF0v8PNsGrpe0maStS9k5tpcDSJoDTAEuGNpmR7tMOPqyId/mwpM/MOTbjBjOOtUTMXClpJskTS+xrWwvKdMPAVuV6bHAA7V1F5VYb/GXkTRd0jxJ85YtWzZY+xARMeJ1qifyLtuLJb0amCPpt/WFti3Jg7Ux22cBZwFMnjx50OqNiBjpOtITsb24/F0KXEJ1TOPhMkxF+bu0FF8MbFNbfVyJ9RaPiIghMuRJRNImkl7ZNQ3sCdwOzAK6zrCaBlxapmcBB5WztHYFnijDXlcAe0ravBxQ37PEIiJiiHRiOGsr4BJJXdv/oe2fSpoLzJR0CHAf8JFSfjawD7AAeAY4GMD2ckknAnNLuRO6DrJHRMTQGPIkYvse4M09xB8F3tdD3MARvdQ1A5gx2G2MiIjW5BfrERHRWJJIREQ0tib92DAiOqQTP+yE/LhzbZCeSERENJYkEhERjSWJREREY0kiERHRWJJIREQ0lrOzholcFj0i1kTpiURERGNJIhER0ViSSERENJYkEhERjSWJREREY0kiERHRWJJIREQ0liQSERGN5ceGEd108oed+VFpDDfpiURERGNJIhER0diwTyKSpki6S9ICSUd3uj0RESPJsD4mImkUcAawB7AImCtplu072rG9jFdHRKxquPdEdgYW2L7H9vPAhcDUDrcpImLEkO1Ot6ExSfsBU2z/bZn/OLCL7SO7lZsOTC+z2wN3NdjcaOCR1WjumiT7smbKvqyZsi+V19oe0z04rIezWmX7LOCs1alD0jzbkwepSR2VfVkzZV/WTNmXvg334azFwDa1+XElFhERQ2C4J5G5wERJ20paHzgAmNXhNkVEjBjDejjL9kpJRwJXAKOAGbbnt2lzqzUctobJvqyZsi9rpuxLH4b1gfWIiOis4T6cFRERHZQkEhERjSWJ9EPShpJulHSLpPmSvtrpNq0OSQsl3SbpZknzOt2epiRtX/ah67FC0uc63a6mJB0l6fbyGvtcp9szEJJmSFoq6fZabP+yLy9KGjanx/ayLydKurW8zq6U9JpOtrFVvezL8ZIW1943+6z2dnJMpG+SBGxi+ylJ6wHXAkfZvr7DTWtE0kJgsu215cdTXZe/WUz1Q9P7Ot2egZK0I9XVFnYGngd+Chxme0FHG9YiSX8BPAWcZ3vHEnsj8CLwHeALtofFF5Ze9mVT2yvK9GeBHWwf1sFmtqSXfTkeeMr2NwdrO+mJ9MOVp8rseuWRzLtmeR/w++GYQIo3AjfYfsb2SuAXwF91uE0ts/1LYHm32J22m1wZoqN62ZcVtdlNGCbv/572pR2SRFogaZSkm4GlwBzbN3S4SavDwJWSbiqXg1kbHABc0OlGrIbbgXdL2lLSxsA+rPoj2ugwSSdJegD4GPCVTrdnNR1ZhudmSNp8dStLEmmB7RdsT6L6RfzOZfhhuHqX7Z2AvYEjSpd32Co/Mv0Q8B+dbktTtu8ETgGupBrKuhl4oZNtilXZ/rLtbYAfAEf2V34NdiawHTAJWAKcuroVJokMgO3HgauBKR1uSmO2F5e/S4FLqMbhh7O9gV/bfrjTDVkdts+2/VbbfwE8Bvyu022KHv0A+OtON6Ip2w+XL8UvAt9lEN7/SSL9kDRG0mZleiOqe5f8tqONakjSJpJe2TUN7Ek1lDKcHcjwHsoCQNKry9/xVMdDftjZFkUXSRNrs1MZpu9/AElb12Y/zCC8/4f1ZU+GyNbAueUMoHWAmbZ/0uE2NbUVcEl1whnrAj+0/dPONqm5kgj3AD7V6bYMgoslbQn8ETii9HqHBUkXALsBoyUtAo6jOqD7r8AY4DJJN9veq3OtbE0v+7KPpO2pzja7D1jjz8yCXvdlN0mTqI6NLmQQ3js5xTciIhrLcFZERDSWJBIREY0liURERGNJIhER0ViSSERENJYkEms9SZZ0am3+C+VCdINR9zmS9huMuvrZzv6S7pR0dbf4JZL2rc3fJenY2vzFkhpdh0vSJyT9v8aNjhEhSSRGgueAv5I0utMNqZM0kN9pHQIcavu93eK/At5R6tsSeBp4e23524HrWmzPqAG0JwJIEomRYSXVvaX/rvuC7j0JSU+Vv7tJ+oWkSyXdI+lkSR8r95a5TdJ2tWreL2mepN9J+suy/ihJ35A0t1zs7lO1ev9b0izgjh7ac2Cp/3ZJp5TYV4B3AWdL+ka3Va6jJJHy98fAGFW2BZ61/VBP9Xbtr6RTJd0CvF3SwWU/bgTeWSu3f1n3Fkm/bO1pj5Egv1iPkeIM4FZJ/3cA67yZ6jLty4F7gO/Z3lnSUcBngM+VchOorkG0HXC1pNcDBwFP2H6bpA2AX0m6spTfCdjR9r31jam62dEpwFuprp91paR9bZ8gaXd6vi/HTcCO5UKU76C6jPzrSrvfAlzXR73/RXVp8xtsf75cEuOHpdwTVNeJ+03ZzleAvWwv7roMUASkJxIjRLknxHnAZwew2lzbS2w/B/ye6iq7ALdRJY4uM22/aPtuqmTzBqrrkh1UbiFwA7Al0HUNphu7J5DibcA1tpeV+4r8AOjzKsulbfOpEtOuZVv/Q5VQ3kE13NVXvS8AF5fpXWrlngd+VNvUr4BzJB0KZNgrXpIkEiPJP1MdW9ikFltJeR9IWgdYv7bsudr0i7X5F1m1F9/92kEGBHzG9qTy2NZ2VxJ6enV2oge/okoKr7T9GHA9f0oi/R0P+YPtfi87X+7kdyzVfU5uKsdfIpJEYuSwvRyYSZVIuiykGr6B6r4k6zWoen9J65TjJK8D7gKuAD6t6pbKSPrzcsHIvtwIvEfS6HKQ+0Cq4an+XEd1Ib1byvytVL2S8VRXaW213htKuS1Lu/fvWiBpO9s32P4KsIzcNCuKHBOJkeZUVr2p0HeBS8uB5Z/SrJdwP9UH9aZU90b/g6TvUQ15/VrVZZOXAfv2VYntJZKOpjoWIeAy25e2sP3rqJLX10s9KyUtBR4o941oqd6y/eOphsMep7o5VpdvlEuiC7iKPyWsGOFyFd+IiGgsw1kREdFYkkhERDSWJBIREY0liURERGNJIhER0ViSSERENJYkEhERjf1/Qym4s8SrTF0AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Test Your Zaka\n",
        "plt.hist(df_clean['ENG Length'], rwidth=0.95)\n",
        "plt.xlabel('Number of Words')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of English Sentence Length')\n",
        "plt.xticks(range(min(df_clean['ENG Length']), max(df_clean['ENG Length'])+1, 2))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "TSn4L7kW9R7g",
        "outputId": "fb1681da-c2ba-4db5-bd6b-9ce70d7ed291"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Test Your Zaka\n",
        "plt.hist(df_clean['FR Length'], rwidth=0.95)\n",
        "plt.xlabel('Number of Words')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of French Sentence Length')\n",
        "plt.xticks(range(min(df_clean['FR Length']), max(df_clean['FR Length']+1), 2))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BDXb2d9ix9DV"
      },
      "source": [
        "Get the maximum length of an english sentence and the maximum length of a french sentence. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BpnBB04U_lHd",
        "outputId": "f5334c5b-baa0-4398-bb4f-7bd3ea2fa41c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Maximum length of an English sentence: 15\n",
            "Maximum length of a French sentence: 21\n"
          ]
        }
      ],
      "source": [
        "#Test Your Zaka\n",
        "max_eng_length = df_clean['ENG Length'].max()\n",
        "max_fr_length = df_clean['FR Length'].max()\n",
        "\n",
        "print('Maximum length of an English sentence:', max_eng_length)\n",
        "print('Maximum length of a French sentence:', max_fr_length)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4s-spsRyGJv"
      },
      "source": [
        "##Preprocessing the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N0ZmIT2GyJMU"
      },
      "source": [
        "In order for the data to be fed to the model, it has to be tokenized and padded. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R0r9z-eErm9H"
      },
      "source": [
        "####Tokenization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5L_zkhfyQuX"
      },
      "source": [
        "**To tokenize english and french sentences, we can use only one tokenizer. True or False?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Z0ZcNOeyauD"
      },
      "source": [
        "It's generally a good practice to use language-specific tokenizers for each language to avoid any mistakes related to the grammer for example. However, True as long as the both languages words are saperated by spaces or the same approach."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "814mKDFiymcY"
      },
      "source": [
        "Tokenize the sentences that we have."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pMTD6Oe1UDTj"
      },
      "outputs": [],
      "source": [
        "df['English']=df['English'].apply(lambda x: 'ST '+x.strip().lower()+' EN')\n",
        "df['French']=df['French'].apply(lambda x: 'ST '+x.strip().lower()+' EN')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aiXlciqFuQzW"
      },
      "outputs": [],
      "source": [
        "#Test Your Zaka\n",
        "# English tokenizer\n",
        "eng_tokenizer = Tokenizer()\n",
        "eng_tokenizer.fit_on_texts(df['English'].values)\n",
        "eng_seq = eng_tokenizer.texts_to_sequences(df['English'].values)\n",
        "\n",
        "# French tokenizer\n",
        "fr_tokenizer = Tokenizer()\n",
        "fr_tokenizer.fit_on_texts(df['French'].values)\n",
        "fr_seq = fr_tokenizer.texts_to_sequences(df['French'].values)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aUN01jDXys9B"
      },
      "source": [
        "**How many unique words do we have in english and in french?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WahkdzKvIlO",
        "outputId": "e405c245-3100-4474-e022-21761ee33d13"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of unique English words: 202\n",
            "Number of unique French words: 346\n"
          ]
        }
      ],
      "source": [
        "#Test Your Zaka\n",
        "num_eng_words=len(eng_tokenizer.word_index)+1\n",
        "num_fr_words=len(fr_tokenizer.word_index)+1\n",
        "\n",
        "print('Number of unique English words:', num_eng_words)\n",
        "print('Number of unique French words:', num_fr_words)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0C2RJjArtJd"
      },
      "source": [
        "####Padding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXdTXMo5y8oB"
      },
      "source": [
        "**What should be the length of the sequences that we have after padding?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wtHQsgXzImq"
      },
      "source": [
        "The max length detected in the dataset which is 21 for French and 15 for English. Then, 2 is added for the \"ST\" start and \"EN\" end char."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hRXayRzVzQD4"
      },
      "source": [
        "Perform padding on the sequences that we have."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oNdO9EZrxvmN"
      },
      "outputs": [],
      "source": [
        "#Test Your Zaka\n",
        "eng_pad_seq = pad_sequences(eng_seq, maxlen=max_eng_length+2, padding='post')\n",
        "fr_pad_seq = pad_sequences(fr_seq, maxlen=max_fr_length+2, padding='post')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JxvvVU3ezUHR"
      },
      "source": [
        "##Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEKujJUEzVux"
      },
      "source": [
        "After preprrocessing the data, we can build our model. Start by building a baseline architecture relying on one directional RNNs, LSTMs, or GRUs. It will be good to lookup how to build Seq2Seq models, there are some new layers that will help you like RepeatVector and TimeDistributed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WmEwaLUFpwF7"
      },
      "outputs": [],
      "source": [
        "fr_pad_seq_noST = np.roll(fr_pad_seq, shift=-1, axis=-1)\n",
        "fr_pad_seq_noST[:,-1] = 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gBwu3o3dgvic"
      },
      "outputs": [],
      "source": [
        "# Encoder\n",
        "E_input = Input(shape=(max_eng_length+2,))                               \n",
        "E_embedding  = Embedding(input_dim=num_eng_words, output_dim=50)(E_input) \n",
        "_, Eh, Ec = LSTM(units=512, return_state=True)(E_embedding)       \n",
        "\n",
        "# Decoder layer definitions\n",
        "decoder_embedding = Embedding(input_dim=num_fr_words, output_dim=50)\n",
        "decoder_lstm = LSTM(512, return_sequences=True, return_state=True)\n",
        "decoder_dense = Dense(num_fr_words, activation='softmax')\n",
        "\n",
        "# Decoder in train mode\n",
        "D_input = Input(shape=(max_fr_length+2,))                         \n",
        "D_embedding  = decoder_embedding(D_input)                                            \n",
        "D_lstm, _, _ = decoder_lstm(D_embedding , initial_state=[Eh, Ec]) #Eh and Ec are the final hidden state and cell state of the encoder           \n",
        "D_output = decoder_dense(D_lstm)                                    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aP10HtNBzpT0"
      },
      "source": [
        "Compile and train the model. \n",
        "**FYI:** While specifying the architecture of your model and the number of epochs for training, keeep in your mind that your model might take A LOT of time to train."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lWw4nBNIFp9D",
        "outputId": "f0bcb439-f766-420f-cdd0-ff17762060bf"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 17)]         0           []                               \n",
            "                                                                                                  \n",
            " input_2 (InputLayer)           [(None, 23)]         0           []                               \n",
            "                                                                                                  \n",
            " embedding (Embedding)          (None, 17, 50)       10100       ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " embedding_1 (Embedding)        (None, 23, 50)       17300       ['input_2[0][0]']                \n",
            "                                                                                                  \n",
            " lstm (LSTM)                    [(None, 512),        1153024     ['embedding[0][0]']              \n",
            "                                 (None, 512),                                                     \n",
            "                                 (None, 512)]                                                     \n",
            "                                                                                                  \n",
            " lstm_1 (LSTM)                  [(None, 23, 512),    1153024     ['embedding_1[0][0]',            \n",
            "                                 (None, 512),                     'lstm[0][1]',                   \n",
            "                                 (None, 512)]                     'lstm[0][2]']                   \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 23, 346)      177498      ['lstm_1[0][0]']                 \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2,510,946\n",
            "Trainable params: 2,510,946\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#Test Your Zaka\n",
        "model = tf.keras.Model(inputs=[E_input, D_input], outputs=D_output)\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),\n",
        "              loss=tf.keras.losses.sparse_categorical_crossentropy,\n",
        "              metrics=[tf.keras.metrics.sparse_categorical_accuracy])    \n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4326QEEiqM8I",
        "outputId": "37d0545f-e3ea-4434-e5f9-bb32598187d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "108/108 [==============================] - 27s 164ms/step - loss: 2.6263 - sparse_categorical_accuracy: 0.4781 - val_loss: 1.8644 - val_sparse_categorical_accuracy: 0.5825\n",
            "Epoch 2/20\n",
            "108/108 [==============================] - 13s 119ms/step - loss: 1.3298 - sparse_categorical_accuracy: 0.6567 - val_loss: 0.9350 - val_sparse_categorical_accuracy: 0.7293\n",
            "Epoch 3/20\n",
            "108/108 [==============================] - 12s 114ms/step - loss: 0.7734 - sparse_categorical_accuracy: 0.7571 - val_loss: 0.6586 - val_sparse_categorical_accuracy: 0.7816\n",
            "Epoch 4/20\n",
            "108/108 [==============================] - 13s 117ms/step - loss: 0.6034 - sparse_categorical_accuracy: 0.7970 - val_loss: 0.5600 - val_sparse_categorical_accuracy: 0.8091\n",
            "Epoch 5/20\n",
            "108/108 [==============================] - 13s 122ms/step - loss: 0.5352 - sparse_categorical_accuracy: 0.8140 - val_loss: 0.5086 - val_sparse_categorical_accuracy: 0.8240\n",
            "Epoch 6/20\n",
            "108/108 [==============================] - 13s 121ms/step - loss: 0.5485 - sparse_categorical_accuracy: 0.8123 - val_loss: 0.4886 - val_sparse_categorical_accuracy: 0.8320\n",
            "Epoch 7/20\n",
            "108/108 [==============================] - 13s 122ms/step - loss: 0.4709 - sparse_categorical_accuracy: 0.8363 - val_loss: 0.4643 - val_sparse_categorical_accuracy: 0.8371\n",
            "Epoch 8/20\n",
            "108/108 [==============================] - 13s 120ms/step - loss: 0.4475 - sparse_categorical_accuracy: 0.8439 - val_loss: 0.4287 - val_sparse_categorical_accuracy: 0.8497\n",
            "Epoch 9/20\n",
            "108/108 [==============================] - 13s 124ms/step - loss: 0.4152 - sparse_categorical_accuracy: 0.8539 - val_loss: 0.4169 - val_sparse_categorical_accuracy: 0.8490\n",
            "Epoch 10/20\n",
            "108/108 [==============================] - 13s 122ms/step - loss: 0.3889 - sparse_categorical_accuracy: 0.8607 - val_loss: 0.3734 - val_sparse_categorical_accuracy: 0.8653\n",
            "Epoch 11/20\n",
            "108/108 [==============================] - 13s 118ms/step - loss: 0.3588 - sparse_categorical_accuracy: 0.8702 - val_loss: 0.3483 - val_sparse_categorical_accuracy: 0.8739\n",
            "Epoch 12/20\n",
            "108/108 [==============================] - 13s 122ms/step - loss: 0.3345 - sparse_categorical_accuracy: 0.8780 - val_loss: 0.3183 - val_sparse_categorical_accuracy: 0.8855\n",
            "Epoch 13/20\n",
            "108/108 [==============================] - 13s 119ms/step - loss: 0.3071 - sparse_categorical_accuracy: 0.8886 - val_loss: 0.2957 - val_sparse_categorical_accuracy: 0.8929\n",
            "Epoch 14/20\n",
            "108/108 [==============================] - 13s 121ms/step - loss: 0.2832 - sparse_categorical_accuracy: 0.8979 - val_loss: 0.2651 - val_sparse_categorical_accuracy: 0.9064\n",
            "Epoch 15/20\n",
            "108/108 [==============================] - 13s 121ms/step - loss: 0.2619 - sparse_categorical_accuracy: 0.9060 - val_loss: 0.2469 - val_sparse_categorical_accuracy: 0.9121\n",
            "Epoch 16/20\n",
            "108/108 [==============================] - 13s 119ms/step - loss: 0.2331 - sparse_categorical_accuracy: 0.9180 - val_loss: 0.2258 - val_sparse_categorical_accuracy: 0.9194\n",
            "Epoch 17/20\n",
            "108/108 [==============================] - 13s 124ms/step - loss: 0.2123 - sparse_categorical_accuracy: 0.9267 - val_loss: 0.1932 - val_sparse_categorical_accuracy: 0.9357\n",
            "Epoch 18/20\n",
            "108/108 [==============================] - 13s 119ms/step - loss: 0.1905 - sparse_categorical_accuracy: 0.9353 - val_loss: 0.2032 - val_sparse_categorical_accuracy: 0.9264\n",
            "Epoch 19/20\n",
            "108/108 [==============================] - 13s 122ms/step - loss: 0.1695 - sparse_categorical_accuracy: 0.9439 - val_loss: 0.1643 - val_sparse_categorical_accuracy: 0.9442\n",
            "Epoch 20/20\n",
            "108/108 [==============================] - 13s 121ms/step - loss: 0.1401 - sparse_categorical_accuracy: 0.9562 - val_loss: 0.1425 - val_sparse_categorical_accuracy: 0.9526\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f89506028e0>"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit(x=[eng_pad_seq, fr_pad_seq], y=np.expand_dims(fr_pad_seq_noST, axis=-1),\n",
        "          batch_size=1024, epochs=20, validation_split=0.2, workers=2, use_multiprocessing=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UoEcxyJztiQ"
      },
      "source": [
        "Define a function that gets an input sentence in english and gives the output sentence in the french language."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fUU_RdCxYpM6"
      },
      "outputs": [],
      "source": [
        "#Test Your Zaka\n",
        "def eng_fr_translation_model1(input):\n",
        "  st_input=remove_punctuation(input)\n",
        "  st_input= 'ST '+st_input.strip().lower()+' EN'\n",
        "  st_input=eng_tokenizer.texts_to_sequences([st_input])\n",
        "  st_input=pad_sequences(st_input, maxlen=max_eng_length+2, padding='post')\n",
        "  \n",
        "  encoder = tf.keras.Model(inputs=E_input, outputs=[Eh, Ec])\n",
        "\n",
        "  Sh_init = Input(shape=(512,))                                          \n",
        "  Sc_init = Input(shape=(512,))                                          \n",
        "  S_input = Input(shape=(1,),)                         \n",
        "  S_embedding  = decoder_embedding(S_input)                                    \n",
        "  S_lstm, Sh, Sc = decoder_lstm(S_embedding , initial_state=[Sh_init, Sc_init]) \n",
        "  S_output = decoder_dense(S_lstm) \n",
        "\n",
        "  sampler = tf.keras.Model(inputs=[S_input, Sh_init, Sc_init], outputs=[S_output, Sh, Sc])\n",
        "\n",
        "  st_h, st_c = encoder.predict(st_input)\n",
        "\n",
        "  st_input = fr_tokenizer.word_index['st']\n",
        "  st_input = np.array([[st_input]])  \n",
        "\n",
        "  prediction_tok = []                     \n",
        "  for i in range(num_fr_words):\n",
        "      probs, st_h, st_c = sampler.predict([st_input, st_h, st_c])\n",
        "      \n",
        "      st_input = probs.argmax(axis=-1)\n",
        "      \n",
        "      token = probs.argmax()\n",
        "      if token != fr_tokenizer.word_index['en']:\n",
        "        prediction_tok.append(token)\n",
        "      \n",
        "      if token == fr_tokenizer.word_index['en']:\n",
        "\n",
        "          break    \n",
        "\n",
        "  words = [fr_tokenizer.index_word[x] for x in prediction_tok if x in fr_tokenizer.index_word]\n",
        "  words=' '.join(words)\n",
        "\n",
        "  return words"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUQIcAjWz3bt"
      },
      "source": [
        "Test the following sentence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "fDmNqnZIQMko",
        "outputId": "85b91cfe-c103-4c6e-c0b2-36d4b9ccaf0a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 363ms/step\n",
            "1/1 [==============================] - 0s 361ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'elle conduit le nouveau camion blanc'"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "input = \"she is driving the truck\"\n",
        "\n",
        "#Test Your Zaka\n",
        "eng_fr_translation_model1(input)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wdI2XhaBz6CN"
      },
      "source": [
        "Try to improve your model by modifying the architecture to take into account bidirectionality which is very useful in Machine Translation. Create a new model called model2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ch28BLsbGnCn"
      },
      "outputs": [],
      "source": [
        "# Encoder\n",
        "E_input_2 = Input(shape=(max_eng_length+2,))                               \n",
        "E_embedding_2 = Embedding(input_dim=num_eng_words, output_dim=50)(E_input_2) \n",
        "_, Eh1_2, Ec1_2, Eh2_2, Ec2_2 = Bidirectional(LSTM(units=512, return_state=True))(E_embedding_2)      \n",
        "Eh_2 = Concatenate()([Eh1_2, Eh2_2])\n",
        "Ec_2 = Concatenate()([Ec1_2, Ec2_2])\n",
        "\n",
        "# Decoder layer definitions\n",
        "decoder_embedding_2 = Embedding(input_dim=num_fr_words, output_dim=50)\n",
        "decoder_lstm_2 = Bidirectional(LSTM(512, return_sequences=True, return_state=True))\n",
        "decoder_dense_2 = Dense(num_fr_words, activation='softmax')\n",
        "\n",
        "# Decoder in train mode\n",
        "D_input_2 = Input(shape=(max_fr_length+2,))                         \n",
        "D_embedding_2 = decoder_embedding_2(D_input_2)                                            \n",
        "D_lstm_2, _, _, _, _ = decoder_lstm_2(D_embedding_2, initial_state=[Eh_2[:, :512], Ec_2[:, :512], Eh_2[:, 512:], Ec_2[:, 512:]])           \n",
        "D_output_2 = decoder_dense_2(D_lstm_2) \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHDvxt9L0C21"
      },
      "source": [
        "compile and train your new model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iK1QvVmaTWI2",
        "outputId": "7bc25660-d437-4b44-fadd-a1f918634aab"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_21\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_50 (InputLayer)          [(None, 17)]         0           []                               \n",
            "                                                                                                  \n",
            " embedding_14 (Embedding)       (None, 17, 50)       10100       ['input_50[0][0]']               \n",
            "                                                                                                  \n",
            " bidirectional_14 (Bidirectiona  [(None, 1024),      2306048     ['embedding_14[0][0]']           \n",
            " l)                              (None, 512),                                                     \n",
            "                                 (None, 512),                                                     \n",
            "                                 (None, 512),                                                     \n",
            "                                 (None, 512)]                                                     \n",
            "                                                                                                  \n",
            " input_51 (InputLayer)          [(None, 23)]         0           []                               \n",
            "                                                                                                  \n",
            " concatenate_14 (Concatenate)   (None, 1024)         0           ['bidirectional_14[0][1]',       \n",
            "                                                                  'bidirectional_14[0][3]']       \n",
            "                                                                                                  \n",
            " concatenate_15 (Concatenate)   (None, 1024)         0           ['bidirectional_14[0][2]',       \n",
            "                                                                  'bidirectional_14[0][4]']       \n",
            "                                                                                                  \n",
            " embedding_15 (Embedding)       (None, 23, 50)       17300       ['input_51[0][0]']               \n",
            "                                                                                                  \n",
            " tf.__operators__.getitem_28 (S  (None, 512)         0           ['concatenate_14[0][0]']         \n",
            " licingOpLambda)                                                                                  \n",
            "                                                                                                  \n",
            " tf.__operators__.getitem_29 (S  (None, 512)         0           ['concatenate_15[0][0]']         \n",
            " licingOpLambda)                                                                                  \n",
            "                                                                                                  \n",
            " tf.__operators__.getitem_30 (S  (None, 512)         0           ['concatenate_14[0][0]']         \n",
            " licingOpLambda)                                                                                  \n",
            "                                                                                                  \n",
            " tf.__operators__.getitem_31 (S  (None, 512)         0           ['concatenate_15[0][0]']         \n",
            " licingOpLambda)                                                                                  \n",
            "                                                                                                  \n",
            " bidirectional_15 (Bidirectiona  [(None, 23, 1024),  2306048     ['embedding_15[0][0]',           \n",
            " l)                              (None, 512),                     'tf.__operators__.getitem_28[0][\n",
            "                                 (None, 512),                    0]',                             \n",
            "                                 (None, 512),                     'tf.__operators__.getitem_29[0][\n",
            "                                 (None, 512)]                    0]',                             \n",
            "                                                                  'tf.__operators__.getitem_30[0][\n",
            "                                                                 0]',                             \n",
            "                                                                  'tf.__operators__.getitem_31[0][\n",
            "                                                                 0]']                             \n",
            "                                                                                                  \n",
            " dense_7 (Dense)                (None, 23, 346)      354650      ['bidirectional_15[0][0]']       \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 4,994,146\n",
            "Trainable params: 4,994,146\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#Test Your Zaka\n",
        "model2 = tf.keras.Model(inputs=[E_input_2, D_input_2], outputs=D_output_2)\n",
        "model2.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),\n",
        "              loss=tf.keras.losses.sparse_categorical_crossentropy,\n",
        "              metrics=[tf.keras.metrics.sparse_categorical_accuracy])    \n",
        "model2.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dRNsurgGucyZ",
        "outputId": "5aaa5240-b147-424f-c6d6-f3570fe42f35"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "108/108 [==============================] - 36s 263ms/step - loss: 2.4997 - sparse_categorical_accuracy: 0.5000 - val_loss: 1.6689 - val_sparse_categorical_accuracy: 0.6042\n",
            "Epoch 2/10\n",
            "108/108 [==============================] - 29s 269ms/step - loss: 1.0996 - sparse_categorical_accuracy: 0.7083 - val_loss: 0.6671 - val_sparse_categorical_accuracy: 0.8075\n",
            "Epoch 3/10\n",
            "108/108 [==============================] - 29s 265ms/step - loss: 0.4187 - sparse_categorical_accuracy: 0.8907 - val_loss: 0.1931 - val_sparse_categorical_accuracy: 0.9642\n",
            "Epoch 4/10\n",
            "108/108 [==============================] - 28s 256ms/step - loss: 0.0890 - sparse_categorical_accuracy: 0.9876 - val_loss: 0.0356 - val_sparse_categorical_accuracy: 0.9964\n",
            "Epoch 5/10\n",
            "108/108 [==============================] - 28s 262ms/step - loss: 0.0224 - sparse_categorical_accuracy: 0.9978 - val_loss: 0.0149 - val_sparse_categorical_accuracy: 0.9987\n",
            "Epoch 6/10\n",
            "108/108 [==============================] - 29s 265ms/step - loss: 0.0110 - sparse_categorical_accuracy: 0.9991 - val_loss: 0.0087 - val_sparse_categorical_accuracy: 0.9993\n",
            "Epoch 7/10\n",
            "108/108 [==============================] - 28s 261ms/step - loss: 0.0067 - sparse_categorical_accuracy: 0.9996 - val_loss: 0.0059 - val_sparse_categorical_accuracy: 0.9996\n",
            "Epoch 8/10\n",
            "108/108 [==============================] - 28s 259ms/step - loss: 0.0045 - sparse_categorical_accuracy: 0.9998 - val_loss: 0.0043 - val_sparse_categorical_accuracy: 0.9997\n",
            "Epoch 9/10\n",
            "108/108 [==============================] - 28s 261ms/step - loss: 0.0033 - sparse_categorical_accuracy: 0.9998 - val_loss: 0.0032 - val_sparse_categorical_accuracy: 0.9998\n",
            "Epoch 10/10\n",
            "108/108 [==============================] - 28s 260ms/step - loss: 0.0025 - sparse_categorical_accuracy: 0.9999 - val_loss: 0.0025 - val_sparse_categorical_accuracy: 0.9999\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7206d9c190>"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model2.fit(x=[eng_pad_seq, fr_pad_seq], y=np.expand_dims(fr_pad_seq_noST, axis=-1),\n",
        "          batch_size=1028, epochs=10, validation_split=0.2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CkpOI2JI0GBx"
      },
      "source": [
        "Define a new function that relies on your new model to make predictions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gulu8OiXTbae"
      },
      "outputs": [],
      "source": [
        "#Test Your Zaka\n",
        "def eng_fr_translation_model2(input):\n",
        "  st_input=remove_punctuation(input)\n",
        "  st_input= 'ST '+st_input.strip().lower()+' EN'\n",
        "  st_input=eng_tokenizer.texts_to_sequences([st_input])\n",
        "  st_input=pad_sequences(st_input, maxlen=max_eng_length+2, padding='post')\n",
        "  encoder = tf.keras.Model(inputs=E_input_2, outputs=[Eh1_2, Ec1_2, Eh2_2, Ec2_2])\n",
        "\n",
        "  Sh_init1 = Input(shape=(512,))                                          \n",
        "  Sc_init1 = Input(shape=(512,))    \n",
        "  Sh_init2 = Input(shape=(512,))                                          \n",
        "  Sc_init2 = Input(shape=(512,))                                       \n",
        "  S_input = Input(shape=(1,),)                         \n",
        "  S_embedding  = decoder_embedding_2(S_input)                                    \n",
        "  S_lstm, Sh1_2, Sc1_2, Sh2_2, Sc2_2 = decoder_lstm_2(S_embedding , initial_state=[Sh_init1, Sc_init1, Sh_init2, Sc_init2]) \n",
        "  S_output = decoder_dense_2(S_lstm) \n",
        "\n",
        "  sampler = tf.keras.Model(inputs=[S_input, Sh_init1, Sc_init1, Sh_init2, Sc_init2], outputs=[S_output, Sh1_2, Sc1_2, Sh2_2, Sc2_2])\n",
        "\n",
        "  st_h1, st_c1, st_h2, st_c2 = encoder.predict(st_input)\n",
        "\n",
        "  st_input = fr_tokenizer.word_index['st']\n",
        "  st_input = np.array([[st_input]])  \n",
        "\n",
        "  prediction_tok = []                     \n",
        "  for i in range(num_fr_words):\n",
        "      probs, st_h1, st_c1, st_h2, st_c2 = sampler.predict([st_input, st_h1, st_c1, st_h2, st_c2])\n",
        "      \n",
        "      st_input = probs.argmax(axis=-1)\n",
        "      \n",
        "      token = probs.argmax()\n",
        "      if token != fr_tokenizer.word_index['en']:\n",
        "        prediction_tok.append(token)\n",
        "      \n",
        "      if token == fr_tokenizer.word_index['en']:\n",
        "\n",
        "          break    \n",
        "\n",
        "  words = [fr_tokenizer.index_word[x] for x in prediction_tok if x in fr_tokenizer.index_word]\n",
        "  words=' '.join(words)\n",
        "\n",
        "  return words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8CO0pO6-UAeE",
        "outputId": "7e0a354b-b7be-4d1f-8bd9-3dcfc402002c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'il conduisait cette nouvelle'"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "input = \"she is driving the truck\"\n",
        "\n",
        "#Test Your Zaka\n",
        "eng_fr_translation_model2(input)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YGeXrjqbZen7"
      },
      "source": [
        "**What is another adjustment in terms of architecture that you might be able to do to improve your model?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bekjOkjbZlBf"
      },
      "source": [
        "\n",
        "we can add a RepeatVector and TimeDistributed Layers. RepeatVector repeats the input layer n-times. TimeDistributed applies a layer to every temporal slice of the input. It helps to keep one-to-one relations with input and its corresponding output.\n",
        "We can also add an Attention Layer. Attention models help relate input sequence units disregarding distance between them in space and time, at the same time make sequence data processing more parallelizable."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "**What are some additional ways that we can do to improve the performance of our model?**"
      ]
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        "Our dataset includes very limited number of unique words, increasing the training set will get us better results. we can also use data augmentation which increases the diversity of the data and help the model better generalize to unseen data. We could also consider fine-tuning. Additionally, as the bi-lstm, did not perform good, we could try another prediction method or a different neurons or layer number. "
      ]
    }
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