text_generation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOKMBsj7zOzJkTBRhN/ugZT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/natarajankrishna/projects/blob/main/text_generation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import string\n",
        "import requests"
      ],
      "metadata": {
        "id": "DGqE6aoGsNOh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = requests.get('https://ocw.mit.edu/ans7870/6/6.006/s08/lecturenotes/files/t8.shakespeare.txt')"
      ],
      "metadata": {
        "id": "BYobKasesOP9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = response.text.split('\\n')\n",
        "len(data)\n",
        "data = \" \".join(data)"
      ],
      "metadata": {
        "id": "FAHGVVi8sVDm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(doc):\n",
        "  tokens = doc.split()\n",
        "  table = str.maketrans('', '', string.punctuation)\n",
        "  tokens = [w.translate(table) for w in tokens]\n",
        "  tokens = [word for word in tokens if word.isalpha()]\n",
        "  tokens = [word.lower() for word in tokens]\n",
        "  return tokens\n",
        "\n",
        "tokens = clean_text(data)\n",
        "len(tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8X4IXf8OsrrM",
        "outputId": "aa5e3807-a265-4578-dad6-76f411a76aa3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "899788"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "length = 50 + 1\n",
        "lines = []\n",
        "\n",
        "for i in range(length, len(tokens)):\n",
        "  seq = tokens[i-length:i]\n",
        "  line = ' '.join(seq)\n",
        "  lines.append(line)\n",
        "  if i > 200000:\n",
        "    break\n",
        "\n",
        "print(len(lines))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s08cAbEks1rf",
        "outputId": "7738c184-752d-4253-e6b0-16d027971ebb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "199951\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM, Embedding\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "metadata": {
        "id": "hLsIwWXvtCnX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(lines)\n",
        "sequences = tokenizer.texts_to_sequences(lines)\n",
        "sequences = np.array(sequences)\n",
        "X, y = sequences[:, :-1], sequences[:,-1]"
      ],
      "metadata": {
        "id": "4wSQXLbmtQDf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_size = len(tokenizer.word_index) + 1\n",
        "y = to_categorical(y, num_classes=vocab_size)\n",
        "seq_length = X.shape[1]"
      ],
      "metadata": {
        "id": "U0Uu9_k4tb2h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(vocab_size, 50, input_length=seq_length))\n",
        "model.add(LSTM(100, return_sequences=True))\n",
        "model.add(LSTM(100))\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(Dense(vocab_size, activation='softmax'))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7SD3gi-8tsSv",
        "outputId": "299304bf-f8bc-44cf-ea7a-e031e916daef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 50, 50)            658250    \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 50, 100)           60400     \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 100)               80400     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 100)               10100     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 13165)             1329665   \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,138,815\n",
            "Trainable params: 2,138,815\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n",
        "model.fit(X, y, batch_size = 256, epochs = 500)"
      ],
      "metadata": {
        "id": "I9dP-eJWc2RR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "seed_text=lines[12343]\n",
        "seed_text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "id": "T4ihYKkLvkti",
        "outputId": "8d75e63b-e409-4d66-935a-12ff5cadd913"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cannot dispraise but in a kind of praise naming thy name blesses an ill report o what a mansion have those vices got which for their habitation chose out thee where beautys veil doth cover every blot and all things turns to fair that eyes can see take heed dear heart'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_text_seq(model, tokenizer, text_seq_length, seed_text, n_words):\n",
        "  text = []\n",
        "\n",
        "  for _ in range(n_words):\n",
        "    encoded = tokenizer.texts_to_sequences([seed_text])[0]\n",
        "    encoded = pad_sequences([encoded], maxlen = text_seq_length, truncating='pre')\n",
        "\n",
        "    y_predict = model.predict_classes(encoded)\n",
        "\n",
        "    predicted_word = ''\n",
        "    for word, index in tokenizer.word_index.items():\n",
        "      if index == y_predict:\n",
        "        predicted_word = word\n",
        "        break\n",
        "    seed_text = seed_text + ' ' + predicted_word\n",
        "    text.append(predicted_word)\n",
        "  return ' '.join(text)"
      ],
      "metadata": {
        "id": "6RL8tiDnvxc_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generate_text_seq(model, tokenizer, seq_length, seed_text, 100)"
      ],
      "metadata": {
        "id": "af7VnzJovzfI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}