Predicting_the_next_word.ipynb

{
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      "source": [
        "<a href=\"https://colab.research.google.com/github/PXDHU/Natural-Language-Processing/blob/main/Predicting_the_next_word.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bFWbEb6uGbN-"
      },
      "source": [
        "#**Predicting the next word**\n",
        "\n",
        "Creating a model that will predict the next word in a text sequence and train it using a corpus of Shakespeare's sonnets, while also creating some helper functions to pre-process the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "BOwsuGQQY9OL",
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          "graded"
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      "source": [
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.utils import to_categorical \n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTxqlHqKHzhr"
      },
      "source": [
        "Dataset:[Shakespeare Sonnets Dataset](https://www.opensourceshakespeare.org/views/sonnets/sonnet_view.php?range=viewrange&sonnetrange1=1&sonnetrange2=154), which contains more than 2000 lines of text extracted from Shakespeare's sonnets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "WZ4qOUzujMP6",
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          "graded"
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e29c7ab-f7e3-4672-ed77-3d21c8022d13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=108jAePKK4R3BVYBbYJZ32JWUwxeMg20K\n",
            "To: /content/sonnets.txt\n",
            "100% 93.6k/93.6k [00:00<00:00, 120MB/s]\n"
          ]
        }
      ],
      "source": [
        "# sonnets.txt\n",
        "!gdown --id 108jAePKK4R3BVYBbYJZ32JWUwxeMg20K"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "Pfd-nYKij5yY",
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          "graded"
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        "outputId": "b59c9ba7-20ea-45ff-c321-49391093e211"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 2159 lines of sonnets\n",
            "\n",
            "The first 5 lines look like this:\n",
            "\n",
            "from fairest creatures we desire increase,\n",
            "that thereby beauty's rose might never die,\n",
            "but as the riper should by time decease,\n",
            "his tender heir might bear his memory:\n",
            "but thou, contracted to thine own bright eyes,\n"
          ]
        }
      ],
      "source": [
        "# Define path for file with sonnets\n",
        "SONNETS_FILE = './sonnets.txt'\n",
        "\n",
        "# Read the data\n",
        "with open('./sonnets.txt') as f:\n",
        "    data = f.read()\n",
        "\n",
        "# Convert to lower case and save as a list\n",
        "corpus = data.lower().split(\"\\n\")\n",
        "\n",
        "print(f\"There are {len(corpus)} lines of sonnets\\n\")\n",
        "print(f\"The first 5 lines look like this:\\n\")\n",
        "for i in range(5):\n",
        "  print(corpus[i])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "imB15zrSNhA1"
      },
      "source": [
        "## Tokenizing the text\n",
        "\n",
        "Fitting the Tokenizer to the corpus and save the total number of words."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "AAhM_qAZk0o5",
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          "graded"
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      "outputs": [],
      "source": [
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(corpus)\n",
        "total_words = len(tokenizer.word_index) + 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 36
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'from fairest creatures we desire increase,'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "corpus[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XFMP4z11O3os"
      },
      "source": [
        "This text directly into the `texts_to_sequences` method will get an unexpected result:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "EMSEhmbzNZCE",
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            "text/plain": [
              "[[],\n",
              " [],\n",
              " [58],\n",
              " [],\n",
              " [],\n",
              " [],\n",
              " [17],\n",
              " [6],\n",
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              " [],\n",
              " [],\n",
              " [],\n",
              " [17],\n",
              " [],\n",
              " [],\n",
              " [],\n",
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              " [6],\n",
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              " [],\n",
              " [],\n",
              " []]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "tokenizer.texts_to_sequences(corpus[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pPZmZtpEPEeI"
      },
      "source": [
        "This happened because `texts_to_sequences` expects a list and we are providing a string. However a string is still and `iterable` in Python so you will get the word index of every character in the string.\n",
        "\n",
        "Instead we need to place the example whithin a list before passing it to the method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "Qmgo-vXhk4nd",
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          "graded"
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c53d0e50-24d8-49f5-c15a-3c42eac4bcd2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[34, 417, 877, 166, 213, 517]]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "tokenizer.texts_to_sequences([corpus[0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "deletable": false,
        "editable": false,
        "id": "kpTy8WmIQ57P",
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          "graded"
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        "outputId": "422edbfc-2ee7-4933-ea7d-5cb0b12dc46f"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[34, 417, 877, 166, 213, 517]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "tokenizer.texts_to_sequences([corpus[0]])[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-oqy9KjXRJ9A"
      },
      "source": [
        "## Generating n_grams\n",
        "\n",
        "This function receives the fitted tokenizer and the corpus (which is a list of strings) and should return a list containing the `n_gram` sequences for each line in the corpus:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "iy4baJMDl6kj",
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          "graded"
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      "outputs": [],
      "source": [
        "def n_gram_seqs(corpus, tokenizer):\n",
        "    \"\"\"\n",
        "    Generates a list of n-gram sequences\n",
        "    \n",
        "    Args:\n",
        "        corpus (list of string): lines of texts to generate n-grams for\n",
        "        tokenizer (object): an instance of the Tokenizer class containing the word-index dictionary\n",
        "    \n",
        "    Returns:\n",
        "        input_sequences (list of int): the n-gram sequences for each line in the corpus\n",
        "    \"\"\"\n",
        "    input_sequences = []\n",
        "\n",
        "    for line in corpus:\n",
        "      token_list = tokenizer.texts_to_sequences([line])[0]\n",
        "\n",
        "      for i in range(1, len(token_list)):\n",
        "        n_gram_sequence = token_list[:i+1]\n",
        "        input_sequences.append(n_gram_sequence)\n",
        "            \n",
        "    return input_sequences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "DlKqW2pfM7G3",
        "tags": [
          "graded"
        ],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d76a526e-f6ed-45ab-e934-1c87f9f4223f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n_gram sequences for first example look like this:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[34, 417],\n",
              " [34, 417, 877],\n",
              " [34, 417, 877, 166],\n",
              " [34, 417, 877, 166, 213],\n",
              " [34, 417, 877, 166, 213, 517]]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# Testing with one example\n",
        "first_example_sequence = n_gram_seqs([corpus[0]], tokenizer)\n",
        "\n",
        "print(\"n_gram sequences for first example look like this:\\n\")\n",
        "first_example_sequence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "wtPpCcBjNc4c",
        "tags": [
          "graded"
        ],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2eb72d7b-3b66-42d7-ecab-13707c088e0c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n_gram sequences for next 3 examples look like this:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[8, 878],\n",
              " [8, 878, 134],\n",
              " [8, 878, 134, 351],\n",
              " [8, 878, 134, 351, 102],\n",
              " [8, 878, 134, 351, 102, 156],\n",
              " [8, 878, 134, 351, 102, 156, 199],\n",
              " [16, 22],\n",
              " [16, 22, 2],\n",
              " [16, 22, 2, 879],\n",
              " [16, 22, 2, 879, 61],\n",
              " [16, 22, 2, 879, 61, 30],\n",
              " [16, 22, 2, 879, 61, 30, 48],\n",
              " [16, 22, 2, 879, 61, 30, 48, 634],\n",
              " [25, 311],\n",
              " [25, 311, 635],\n",
              " [25, 311, 635, 102],\n",
              " [25, 311, 635, 102, 200],\n",
              " [25, 311, 635, 102, 200, 25],\n",
              " [25, 311, 635, 102, 200, 25, 278]]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Testing with a bigger corpus\n",
        "next_3_examples_sequence = n_gram_seqs(corpus[1:4], tokenizer)\n",
        "\n",
        "print(\"n_gram sequences for next 3 examples look like this:\\n\")\n",
        "next_3_examples_sequence"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dx3V_RjFWQSu"
      },
      "source": [
        "Applying the `n_gram_seqs` transformation to the whole corpus and save the maximum sequence length to use it later:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "laMwiRUpmuSd",
        "tags": [
          "graded"
        ],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e18b13d0-2fc7-41f2-edd5-db7f0ed70c0e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n_grams of input_sequences have length: 15462\n",
            "maximum length of sequences is: 11\n"
          ]
        }
      ],
      "source": [
        "# grader-required-cell\n",
        "\n",
        "# Apply the n_gram_seqs transformation to the whole corpus\n",
        "input_sequences = n_gram_seqs(corpus, tokenizer)\n",
        "\n",
        "# Save max length \n",
        "max_sequence_len = max([len(x) for x in input_sequences])\n",
        "\n",
        "print(f\"n_grams of input_sequences have length: {len(input_sequences)}\")\n",
        "print(f\"maximum length of sequences is: {max_sequence_len}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHY7HroqWq12"
      },
      "source": [
        "## Add padding to the sequences\n",
        "The `pad_seqs` function which will pad any given sequences to the desired maximum length. This function receives a list of sequences and should return a numpy array with the padded sequences: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "cellView": "code",
        "deletable": false,
        "id": "WW1-qAZaWOhC",
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          "graded"
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      },
      "outputs": [],
      "source": [
        "def pad_seqs(input_sequences, maxlen):\n",
        "    \"\"\"\n",
        "    Pads tokenized sequences to the same length\n",
        "    \n",
        "    Args:\n",
        "        input_sequences (list of int): tokenized sequences to pad\n",
        "        maxlen (int): maximum length of the token sequences\n",
        "    \n",
        "    Returns:\n",
        "        padded_sequences (array of int): tokenized sequences padded to the same length\n",
        "    \"\"\"\n",
        "   \n",
        "    padded_sequences = pad_sequences(input_sequences, maxlen=maxlen, padding='pre')\n",
        "    \n",
        "    return padded_sequences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "IqVQ0pb3YHLr",
        "tags": [
          "graded"
        ],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cff4b74e-841a-44a8-ee0c-c4d79d8ed4b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0,   0,   0,   0,  34, 417],\n",
              "       [  0,   0,   0,  34, 417, 877],\n",
              "       [  0,   0,  34, 417, 877, 166],\n",
              "       [  0,  34, 417, 877, 166, 213],\n",
              "       [ 34, 417, 877, 166, 213, 517]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Testing function with the n_grams_seq of the first example\n",
        "first_padded_seq = pad_seqs(first_example_sequence, max([len(x) for x in first_example_sequence]))\n",
        "first_padded_seq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "j56_UCOBYzZt",
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          "graded"
        ],
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a93124e4-3e4b-4321-fd1e-353b145974c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0,   0,   0,   0,   0,   0,   8, 878],\n",
              "       [  0,   0,   0,   0,   0,   8, 878, 134],\n",
              "       [  0,   0,   0,   0,   8, 878, 134, 351],\n",
              "       [  0,   0,   0,   8, 878, 134, 351, 102],\n",
              "       [  0,   0,   8, 878, 134, 351, 102, 156],\n",
              "       [  0,   8, 878, 134, 351, 102, 156, 199],\n",
              "       [  0,   0,   0,   0,   0,   0,  16,  22],\n",
              "       [  0,   0,   0,   0,   0,  16,  22,   2],\n",
              "       [  0,   0,   0,   0,  16,  22,   2, 879],\n",
              "       [  0,   0,   0,  16,  22,   2, 879,  61],\n",
              "       [  0,   0,  16,  22,   2, 879,  61,  30],\n",
              "       [  0,  16,  22,   2, 879,  61,  30,  48],\n",
              "       [ 16,  22,   2, 879,  61,  30,  48, 634],\n",
              "       [  0,   0,   0,   0,   0,   0,  25, 311],\n",
              "       [  0,   0,   0,   0,   0,  25, 311, 635],\n",
              "       [  0,   0,   0,   0,  25, 311, 635, 102],\n",
              "       [  0,   0,   0,  25, 311, 635, 102, 200],\n",
              "       [  0,   0,  25, 311, 635, 102, 200,  25],\n",
              "       [  0,  25, 311, 635, 102, 200,  25, 278]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Testing function with the n_grams_seq of the next 3 examples\n",
        "next_3_padded_seq = pad_seqs(next_3_examples_sequence, max([len(s) for s in next_3_examples_sequence]))\n",
        "next_3_padded_seq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "rgK-Q_micEYA",
        "tags": [
          "graded"
        ],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9956057e-d5ab-4f1f-e16b-6a162c6a48b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "padded corpus has shape: (15462, 11)\n"
          ]
        }
      ],
      "source": [
        "# Padding the whole corpus\n",
        "input_sequences = pad_seqs(input_sequences, max_sequence_len)\n",
        "\n",
        "print(f\"padded corpus has shape: {input_sequences.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZbOidyPrXxf7"
      },
      "source": [
        "## Split the data into features and labels\n",
        "This function expects the padded n_gram sequences as input and should return a tuple containing the features and the one hot encoded labels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "code",
        "deletable": false,
        "id": "9WGGbYdnZdmJ",
        "tags": [
          "graded"
        ]
      },
      "outputs": [],
      "source": [
        "def features_and_labels(input_sequences, total_words):\n",
        "    \"\"\"\n",
        "    Generates features and labels from n-grams\n",
        "    \n",
        "    Args:\n",
        "        input_sequences (list of int): sequences to split features and labels from\n",
        "        total_words (int): vocabulary size\n",
        "    \n",
        "    Returns:\n",
        "        features, one_hot_labels (array of int, array of int): arrays of features and one-hot encoded labels\n",
        "    \"\"\"\n",
        "    \n",
        "    features = input_sequences[:,:-1]\n",
        "    labels = input_sequences[:,-1]\n",
        "    one_hot_labels = to_categorical(labels, num_classes=total_words)\n",
        "\n",
        "    return features, one_hot_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "23DolaBRaIAZ",
        "tags": [
          "graded"
        ],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c854cbd3-c21d-459a-ce59-8b274a8807e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "labels have shape: (5, 3211)\n",
            "\n",
            "features look like this:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0,   0,   0,   0,  34],\n",
              "       [  0,   0,   0,  34, 417],\n",
              "       [  0,   0,  34, 417, 877],\n",
              "       [  0,  34, 417, 877, 166],\n",
              "       [ 34, 417, 877, 166, 213]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Testing function with the padded n_grams_seq of the first example\n",
        "first_features, first_labels = features_and_labels(first_padded_seq, total_words)\n",
        "\n",
        "print(f\"labels have shape: {first_labels.shape}\")\n",
        "print(\"\\nfeatures look like this:\\n\")\n",
        "first_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "deletable": false,
        "editable": false,
        "id": "GRTuLEt3bRKa",
        "tags": [
          "graded"
        ],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7db79254-cfc2-4974-cf2c-5ecaaaaca3bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "features have shape: (15462, 10)\n",
            "labels have shape: (15462, 3211)\n"
          ]
        }
      ],
      "source": [
        "# Splitting the whole corpus\n",
        "features, labels = features_and_labels(input_sequences, total_words)\n",
        "\n",
        "print(f\"features have shape: {features.shape}\")\n",
        "print(f\"labels have shape: {labels.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltxaOCE_aU6J"
      },
      "source": [
        "## Creating the model\n",
        "\n",
        "Model architecture capable of achieving an accuracy of at least 80%.\n",
        "\n",
        "- An appropriate `output_dim` for the first layer (Embedding) is 100.\n",
        "- A Bidirectional LSTM \n",
        "- The last layer with the same number of units as the total number of words in the corpus and a softmax activation function.\n",
        "- Only two layers (excluding the Embedding),just small architectures first."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "code",
        "deletable": false,
        "id": "XrE6kpJFfvRY",
        "tags": [
          "graded"
        ]
      },
      "outputs": [],
      "source": [
        "def create_model(total_words, max_sequence_len):\n",
        "    \"\"\"\n",
        "    Creates a text generator model\n",
        "    \n",
        "    Args:\n",
        "        total_words (int): size of the vocabulary for the Embedding layer input\n",
        "        max_sequence_len (int): length of the input sequences\n",
        "    \n",
        "    Returns:\n",
        "        model (tf.keras Model): the text generator model\n",
        "    \"\"\"\n",
        "    model = Sequential()\n",
        "    \n",
        "    model.add(Embedding(total_words, 100, input_length=max_sequence_len-1)),\n",
        "    model.add(Bidirectional(LSTM(150)))\n",
        "    model.add(Dense(total_words, activation = 'softmax'))\n",
        "\n",
        "\n",
        "    \n",
        "    model.compile(loss='categorical_crossentropy',\n",
        "                  optimizer='adam',\n",
        "                  metrics=['accuracy'])\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0IpX_Gu_gISk",
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e0743a5-b749-45bc-b1d5-ec6d3ad54d60"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "484/484 [==============================] - 24s 30ms/step - loss: 6.8840 - accuracy: 0.0227\n",
            "Epoch 2/50\n",
            "484/484 [==============================] - 5s 11ms/step - loss: 6.4265 - accuracy: 0.0320\n",
            "Epoch 3/50\n",
            "484/484 [==============================] - 4s 9ms/step - loss: 6.1963 - accuracy: 0.0407\n",
            "Epoch 4/50\n",
            "484/484 [==============================] - 5s 11ms/step - loss: 5.9619 - accuracy: 0.0475\n",
            "Epoch 5/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 5.6805 - accuracy: 0.0595\n",
            "Epoch 6/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 5.3456 - accuracy: 0.0716\n",
            "Epoch 7/50\n",
            "484/484 [==============================] - 5s 11ms/step - loss: 4.9645 - accuracy: 0.0891\n",
            "Epoch 8/50\n",
            "484/484 [==============================] - 5s 11ms/step - loss: 4.5568 - accuracy: 0.1166\n",
            "Epoch 9/50\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 4.1455 - accuracy: 0.1618\n",
            "Epoch 10/50\n",
            "484/484 [==============================] - 4s 9ms/step - loss: 3.7530 - accuracy: 0.2256\n",
            "Epoch 11/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 3.3705 - accuracy: 0.2892\n",
            "Epoch 12/50\n",
            "484/484 [==============================] - 5s 11ms/step - loss: 3.0440 - accuracy: 0.3558\n",
            "Epoch 13/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 2.7472 - accuracy: 0.4142\n",
            "Epoch 14/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 2.4777 - accuracy: 0.4747\n",
            "Epoch 15/50\n",
            "484/484 [==============================] - 5s 10ms/step - loss: 2.2433 - accuracy: 0.5175\n",
            "Epoch 16/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 2.0316 - accuracy: 0.5718\n",
            "Epoch 17/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 1.8496 - accuracy: 0.6090\n",
            "Epoch 18/50\n",
            "484/484 [==============================] - 4s 9ms/step - loss: 1.6783 - accuracy: 0.6500\n",
            "Epoch 19/50\n",
            "484/484 [==============================] - 4s 7ms/step - loss: 1.5316 - accuracy: 0.6821\n",
            "Epoch 20/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 1.4018 - accuracy: 0.7120\n",
            "Epoch 21/50\n",
            "484/484 [==============================] - 5s 10ms/step - loss: 1.2808 - accuracy: 0.7378\n",
            "Epoch 22/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 1.1768 - accuracy: 0.7586\n",
            "Epoch 23/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 1.0905 - accuracy: 0.7737\n",
            "Epoch 24/50\n",
            "484/484 [==============================] - 4s 7ms/step - loss: 1.0157 - accuracy: 0.7890\n",
            "Epoch 25/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 0.9500 - accuracy: 0.8035\n",
            "Epoch 26/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 0.8896 - accuracy: 0.8115\n",
            "Epoch 27/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 0.8443 - accuracy: 0.8209\n",
            "Epoch 28/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 0.8033 - accuracy: 0.8276\n",
            "Epoch 29/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 0.7722 - accuracy: 0.8319\n",
            "Epoch 30/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 0.7450 - accuracy: 0.8366\n",
            "Epoch 31/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 0.7148 - accuracy: 0.8382\n",
            "Epoch 32/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 0.6868 - accuracy: 0.8413\n",
            "Epoch 33/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 0.6768 - accuracy: 0.8419\n",
            "Epoch 34/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 0.6612 - accuracy: 0.8430\n",
            "Epoch 35/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 0.6471 - accuracy: 0.8440\n",
            "Epoch 36/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 0.6351 - accuracy: 0.8463\n",
            "Epoch 37/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 0.6310 - accuracy: 0.8453\n",
            "Epoch 38/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 0.6246 - accuracy: 0.8449\n",
            "Epoch 39/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 0.6109 - accuracy: 0.8479\n",
            "Epoch 40/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 0.6012 - accuracy: 0.8482\n",
            "Epoch 41/50\n",
            "484/484 [==============================] - 4s 7ms/step - loss: 0.5955 - accuracy: 0.8482\n",
            "Epoch 42/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 0.5968 - accuracy: 0.8469\n",
            "Epoch 43/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 0.5885 - accuracy: 0.8467\n",
            "Epoch 44/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 0.5835 - accuracy: 0.8490\n",
            "Epoch 45/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 0.5843 - accuracy: 0.8483\n",
            "Epoch 46/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 0.5808 - accuracy: 0.8488\n",
            "Epoch 47/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 0.5733 - accuracy: 0.8485\n",
            "Epoch 48/50\n",
            "484/484 [==============================] - 4s 8ms/step - loss: 0.5748 - accuracy: 0.8482\n",
            "Epoch 49/50\n",
            "484/484 [==============================] - 3s 7ms/step - loss: 0.5680 - accuracy: 0.8487\n",
            "Epoch 50/50\n",
            "484/484 [==============================] - 4s 7ms/step - loss: 0.5640 - accuracy: 0.8476\n"
          ]
        }
      ],
      "source": [
        "# Trainiing the model\n",
        "model = create_model(total_words, max_sequence_len)\n",
        "history = model.fit(features, labels, epochs=50, verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1fXTEO3GJ282",
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "outputId": "22b41c07-904e-4a95-c138-d03cb66f8e4e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Training curves\n",
        "\n",
        "acc = history.history['accuracy']\n",
        "loss = history.history['loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'b', label='Training accuracy')\n",
        "plt.title('Training accuracy')\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'b', label='Training Loss')\n",
        "plt.title('Training loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wdsMszk9zBs_"
      },
      "source": [
        "## Model in action"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Vc6PHgxa6Hm",
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f4e971d-d44e-4cf6-830f-dc08c55e9193"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help me Obi Wan Kenobi, you're my only hope then my argument more too still to die to prove each wit prove ' forth loss of thee do have more more new still new spent fickle worth poison dumb that best stol'n is it contains friend still me untrue well her such place of thine hate doth call it ' not skill fair lovest hate so lark lovest is hate to time's edge that brought to time's part ' leave thee eye prove good his telling heart taken taken thee back the second state of her minds to the sun day by day lie to me do bred friend\n"
          ]
        }
      ],
      "source": [
        "seed_text = \"Help me Obi Wan Kenobi, you're my only hope\"\n",
        "next_words = 100\n",
        "  \n",
        "for _ in range(next_words):\n",
        "    # Converting the text into sequences\n",
        "    token_list = tokenizer.texts_to_sequences([seed_text])[0]\n",
        "    # Padding the sequences\n",
        "    token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')\n",
        "    # Getting the probabilities of predicting a word\n",
        "    predicted = model.predict(token_list, verbose=0)\n",
        "    # Choose the next word based on the maximum probability\n",
        "    predicted = np.argmax(predicted, axis=-1).item()\n",
        "    # Get the actual word from the word index\n",
        "    output_word = tokenizer.index_word[predicted]\n",
        "    # Appending to the current text\n",
        "    seed_text += \" \" + output_word\n",
        "\n",
        "print(seed_text)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "name": "Predicting_the_next_word.ipynb",
      "include_colab_link": true
    },
    "dlai_version": "1.2.0",
    "jupytext": {
      "main_language": "python"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}