Recurrent Reural Network (RNN).ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyOGr29JU+J4CsWbP4skXCpq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SURESHBEEKHANI/-Implementation-of-RNNs-and-LSTM/blob/main/Recurrent%20Reural%20Network%20(RNN).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building a Recurrent Neural Network (RNN)\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "mfBdRDepPcwP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "VWYvPmfDLglL"
      },
      "outputs": [],
      "source": [
        "# Import the NumPy library for numerical operations, using 'np' as a shorthand\n",
        "import numpy as np\n",
        "\n",
        "# Import the pandas library for data manipulation and analysis, using 'pd' as a shorthand\n",
        "import pandas as pd\n",
        "\n",
        "# Import the TensorFlow library for building and training machine learning models, using 'tf' as a shorthand\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "# Import specific layers from TensorFlow's Keras module for constructing neural networks\n",
        "from tensorflow.keras.layers import Dense, SimpleRNN, Embedding\n",
        "\n",
        "# Import the Tokenizer class from TensorFlow's Keras preprocessing module for converting text to sequences of tokens\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "\n",
        "# Import the pad_sequences function from TensorFlow's Keras preprocessing module for padding sequences to a uniform length\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# Import the 'itertools' library to help with list operations, such as flattening nested lists\n",
        "import itertools\n",
        "\n",
        "# Summary:\n",
        "# This code imports libraries and modules necessary for data manipulation, machine learning model building, and text processing.\n",
        "# NumPy and pandas are used for numerical operations and data handling. TensorFlow and its Keras module are used for creating and training neural networks.\n",
        "# The 'itertools' library is imported for list operations such as flattening nested lists.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#  Load the data"
      ],
      "metadata": {
        "id": "YCVe-vqkZiNX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the data from a CSV file called \"data.csv\" into a pandas DataFrame\n",
        "data = pd.read_csv(\"data.csv\")\n",
        "\n",
        "# Convert the DataFrame into a list of lists, where each inner list represents a row of the DataFrame\n",
        "list_of_lists = data.values.tolist()\n",
        "\n",
        "# Flatten the list of lists into a single list containing all the elements\n",
        "flat_list = list(itertools.chain.from_iterable(list_of_lists))\n",
        "\n",
        "# Print the flattened list to view all the data in a single, combined list\n",
        "print(flat_list)\n",
        "\n",
        "# Summary:\n",
        "# This code reads data from a CSV file into a DataFrame, converts the DataFrame into a list of lists,\n",
        "# flattens that list of lists into a single list, and then prints the resulting flat list.\n"
      ],
      "metadata": {
        "id": "92VH2s_ZYRQV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7233a4a7-530c-4612-9a99-6c9f4c380776"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['\"Machine learning algorithms analyze vast amounts of data.\"', '\"AI technologies drive innovation across different sectors.\"', '\"Machine learning models predict outcomes with high accuracy.\"', '\"AI systems can automate complex tasks effectively.\"', '\"Machine learning helps in personalized recommendations and ads.\"', '\"AI advancements are transforming how we interact with technology.\"', '\"Machine learning enhances decision-making through data-driven insights.\"', '\"AI and machine learning reshape traditional business processes.\"', '\"Machine learning models adapt and learn from new data.\"', '\"AI systems provide real-time analysis for quick decisions.\"', '\"Machine learning techniques improve over time with more data.\"', '\"AI applications streamline operations in diverse fields.\"', '\"Machine learning aids in detecting patterns and anomalies.\"', '\"AI-driven tools offer personalized user experiences.\"', '\"Machine learning automates routine tasks with precision.\"', '\"AI technologies support advanced data analytics and visualization.\"', '\"Machine learning enhances predictive maintenance in manufacturing.\"', '\"AI algorithms help in natural language understanding.\"', '\"Machine learning assists in financial forecasting and planning.\"', '\"AI research explores new frontiers in computer science.\"', '\"Machine learning models improve healthcare diagnostics and treatment.\"', '\"AI systems enhance cybersecurity measures and threat detection.\"', '\"Machine learning enables smarter recommendations on e-commerce platforms.\"', '\"AI applications optimize supply chain management and logistics.\"', '\"Machine learning accelerates drug discovery and development.\"', '\"AI technologies foster innovations in autonomous vehicles.\"', '\"Machine learning helps in customer behavior analysis.\"', '\"AI-driven solutions enhance human-computer interaction interfaces.\"', '\"Machine learning models support real-time language translation.\"', '\"AI advancements lead to smarter virtual assistants and chatbots.\"', '\"Machine learning improves accuracy in image and speech recognition.\"', '\"AI systems facilitate personalized marketing strategies.\"', '\"Machine learning algorithms optimize energy consumption in buildings.\"', '\"AI-driven analytics offer deeper insights into market trends.\"', '\"Machine learning enhances fraud detection in financial transactions.\"', '\"AI technologies contribute to advancements in robotics.\"', '\"Machine learning models support autonomous decision-making systems.\"', '\"AI applications aid in weather forecasting and climate analysis.\"', '\"Machine learning helps in optimizing manufacturing processes.\"', '\"AI systems improve efficiency in customer service operations.\"', '\"Machine learning enhances personalization in digital content.\"', '\"AI-driven platforms support advanced research and development.\"', '\"Machine learning aids in predicting and mitigating risks.\"', '\"AI technologies advance computational creativity and design.\"', '\"Machine learning improves accuracy in medical imaging.\"', '\"AI systems enable more effective data-driven strategies.\"', '\"Machine learning models enhance traffic management systems.\"', '\"AI-driven solutions streamline business operations and workflows.\"', '\"Machine learning supports innovation in educational technologies.\"', '\"AI technologies contribute to smarter urban planning.\"', '\"Machine learning models assist in targeted advertising.\"', '\"AI systems enhance user experience in gaming applications.\"', '\"Machine learning helps in detecting financial market trends.\"', '\"AI-driven platforms offer personalized health recommendations.\"', '\"Machine learning improves the efficiency of resource allocation.\"', '\"AI technologies support advances in human resources management.\"', '\"Machine learning models aid in criminal investigation and prevention.\"', '\"AI applications enhance precision in agricultural practices.\"', '\"Machine learning supports advancements in personalized medicine.\"', '\"AI systems help in automating legal document analysis.\"', '\"Machine learning improves the accuracy of search engines.\"', '\"AI-driven tools assist in financial portfolio management.\"', '\"Machine learning enhances the quality of digital content creation.\"', '\"AI technologies optimize customer relationship management systems.\"', '\"Machine learning models improve predictive analytics in various fields.\"', '\"AI systems streamline data processing and management tasks.\"', '\"Machine learning supports innovations in virtual and augmented reality.\"', '\"AI-driven solutions enhance smart home technologies.\"', '\"Machine learning improves the accuracy of weather predictions.\"', '\"AI technologies advance research in neuroscience and brain studies.\"', '\"Machine learning models contribute to advancements in genomics.\"', '\"AI systems facilitate personalized learning experiences in education.\"', '\"Machine learning enhances automation in administrative tasks.\"', '\"AI-driven platforms offer insights into consumer behavior patterns.\"', '\"Machine learning improves the efficiency of logistics and supply chains.\"', '\"AI technologies support advancements in telecommunication networks.\"', '\"Machine learning models assist in real-time financial analysis.\"', '\"AI systems contribute to the development of smart cities.\"', '\"Machine learning aids in optimizing healthcare operations.\"', '\"AI-driven solutions enhance user engagement in digital platforms.\"', '\"Machine learning improves the accuracy of sentiment analysis.\"', '\"AI technologies support research in artificial general intelligence.\"', '\"Machine learning models enhance fraud detection systems.\"', '\"AI systems optimize resource management in various industries.\"', '\"Machine learning supports advancements in autonomous drones.\"', '\"AI-driven tools assist in optimizing marketing campaigns.\"', '\"Machine learning improves predictive maintenance in transportation.\"', '\"AI technologies contribute to the development of intelligent agents.\"', '\"Machine learning models enhance real-time data analytics.\"', '\"AI systems facilitate personalized recommendations in streaming services.\"', '\"Machine learning improves efficiency in research and development.\"', '\"AI-driven platforms support advanced data visualization techniques.\"', '\"Machine learning models assist in detecting cyber threats.\"', '\"AI technologies advance natural language processing capabilities.\"', '\"Machine learning enhances precision in financial forecasting.\"', '\"AI systems optimize supply chain and inventory management.\"', '\"Machine learning models improve customer experience and satisfaction.\"', '\"AI-driven solutions facilitate smarter energy management.\"', '\"Machine learning supports advancements in facial recognition technology.\"', '\"AI technologies contribute to innovations in wearable devices.\"', '\"Machine learning models enhance real-time speech translation.\"', '\"AI systems improve accuracy in medical diagnostics and treatment.\"', '\"Machine learning assists in automating content moderation.\"', '\"AI-driven tools optimize performance in digital marketing.\"', '\"Machine learning models support advancements in precision agriculture.\"', '\"AI technologies advance research in machine vision and robotics.\"', '\"Machine learning enhances efficiency in data-driven decision-making.\"', '\"AI systems contribute to the development of smart infrastructure.\"', '\"Machine learning improves predictive analytics for business strategies.\"', '\"AI-driven solutions assist in optimizing e-commerce operations.\"', '\"Machine learning models support advancements in autonomous vehicles.\"', '\"AI technologies enhance customer support and service automation.\"', '\"Machine learning aids in personalized fitness and wellness plans.\"', '\"AI systems facilitate advanced research in artificial intelligence.\"', '\"Machine learning improves efficiency in financial risk management.\"', '\"AI-driven platforms enhance personalization in digital experiences.\"', '\"Machine learning models assist in optimizing logistics and delivery.\"', '\"AI technologies advance research in quantum computing.\"', '\"Machine learning supports innovations in intelligent transportation systems.\"', '\"AI systems improve the accuracy of diagnostic tools.\"', '\"Machine learning enhances the effectiveness of targeted marketing.\"', '\"AI-driven solutions optimize resource allocation in industries.\"', '\"Machine learning models assist in fraud prevention and detection.\"', '\"AI technologies contribute to advancements in cognitive computing.\"', '\"Machine learning improves efficiency in public services and administration.\"', '\"AI systems support innovations in computer vision applications.\"', '\"Machine learning models enhance customer relationship management.\"', '\"AI-driven tools assist in real-time data processing.\"', '\"Machine learning supports advancements in predictive analytics.\"', '\"AI technologies advance research in human-robot interaction.\"', '\"Machine learning improves accuracy in speech recognition systems.\"', '\"AI systems facilitate smarter decision-making in various sectors.\"', '\"Machine learning models assist in optimizing production processes.\"', '\"AI-driven solutions enhance personalization in user interfaces.\"', '\"Machine learning improves efficiency in resource management.\"', '\"AI technologies contribute to advancements in healthcare technologies.\"', '\"Machine learning models support innovations in digital security.\"', '\"AI systems enhance the capabilities of virtual assistants.\"', '\"Machine learning aids in optimizing energy efficiency in buildings.\"', '\"AI-driven platforms support advancements in autonomous systems.\"', '\"Machine learning models enhance accuracy in medical research.\"', '\"AI technologies improve efficiency in business operations.\"', '\"Machine learning supports personalized recommendations in digital media.\"', '\"AI systems facilitate advancements in machine translation.\"', '\"Machine learning models assist in real-time analytics.\"', '\"AI-driven solutions enhance the effectiveness of smart devices.\"', '\"Machine learning improves decision-making in financial services.\"', '\"AI technologies contribute to innovations in artificial intelligence research.\"', '\"Machine learning models assist in optimizing logistics operations.\"', '\"AI systems enhance personalization in educational technology.\"', '\"Machine learning aids in detecting anomalies in large datasets.\"', '\"AI-driven platforms support advancements in human-computer interaction.\"', '\"Machine learning models enhance accuracy in predictive modeling.\"', '\"AI technologies improve efficiency in content creation and management.\"', '\"Machine learning supports innovations in autonomous robotics.\"', '\"AI systems facilitate advancements in data-driven strategies.\"', '\"Machine learning models assist in optimizing marketing efforts.\"', '\"AI-driven solutions enhance real-time data analysis.\"', '\"Machine learning improves accuracy in recommendation systems.\"', '\"AI technologies contribute to advancements in healthcare diagnostics.\"', '\"Machine learning models support innovations in financial forecasting.\"', '\"AI systems enhance efficiency in operational processes.\"', '\"Machine learning aids in optimizing supply chain management.\"', '\"AI-driven platforms support advancements in data science.\"', '\"Machine learning models improve accuracy in sentiment analysis.\"', '\"AI technologies enhance the capabilities of smart home devices.\"', '\"Machine learning supports innovations in machine learning algorithms.\"', '\"AI systems facilitate advancements in data visualization.\"', '\"Machine learning models assist in optimizing business workflows.\"', '\"AI-driven solutions improve efficiency in customer service.\"', '\"Machine learning enhances the accuracy of financial predictions.\"', '\"AI technologies contribute to advancements in autonomous systems.\"', '\"Machine learning models support innovations in data processing.\"', '\"AI systems enhance real-time decision-making capabilities.\"', '\"Machine learning aids in optimizing digital marketing strategies.\"', '\"AI-driven platforms facilitate advancements in virtual reality.\"', '\"Machine learning improves accuracy in predictive analytics.\"', '\"AI technologies support innovations in intelligent systems.\"', '\"Machine learning models assist in optimizing resource utilization.\"', '\"AI systems enhance efficiency in manufacturing processes.\"', '\"Machine learning aids in real-time fraud detection.\"', '\"AI-driven solutions improve personalization in user experiences.\"', '\"Machine learning supports advancements in automated systems.\"', '\"AI technologies contribute to innovations in data analytics.\"', '\"Machine learning models enhance accuracy in image recognition.\"', '\"AI systems facilitate smarter decision-making in various industries.\"', '\"Machine learning improves efficiency in supply chain operations.\"', '\"AI-driven platforms support advancements in computational intelligence.\"', '\"Machine learning models assist in real-time data processing.\"', '\"AI technologies enhance the effectiveness of smart systems.\"', '\"Machine learning aids in optimizing content recommendations.\"', '\"AI systems improve efficiency in customer engagement.\"', '\"Machine learning supports innovations in predictive maintenance.\"', '\"AI-driven solutions enhance accuracy in financial analysis.\"', '\"Machine learning models assist in optimizing business strategies.\"', '\"AI technologies contribute to advancements in machine learning research.\"', '\"Machine learning improves accuracy in speech and text analysis.\"', '\"AI systems enhance real-time analytics capabilities.\"', '\"Machine learning supports advancements in intelligent algorithms.\"', '\"AI-driven platforms facilitate improvements in user interfaces.\"', '\"Machine learning models assist in optimizing energy management.\"', '\"AI technologies enhance efficiency in various industrial applications.\"', '\"Machine learning aids in real-time threat detection.\"', '\"AI systems improve the accuracy of diagnostic tools.\"', '\"Machine learning supports innovations in intelligent automation.\"', '\"AI-driven solutions enhance personalization in customer interactions.\"', '\"Machine learning models assist in optimizing production efficiency.\"', '\"AI technologies contribute to advancements in data-driven decision-making.\"', '\"Machine learning improves accuracy in predictive modeling.\"', '\"AI systems enhance efficiency in digital content management.\"', '\"Machine learning aids in optimizing logistics and distribution.\"', '\"AI-driven platforms support advancements in computational research.\"', '\"Machine learning models enhance real-time decision-making.\"', '\"AI technologies improve efficiency in operational workflows.\"', '\"Machine learning supports innovations in artificial intelligence.\"', '\"AI systems facilitate advancements in smart technologies.\"', '\"Machine learning improves accuracy in recommendation algorithms.\"', '\"AI-driven solutions enhance the effectiveness of digital tools.\"', '\"Machine learning models assist in optimizing business operations.\"', '\"AI technologies contribute to advancements in intelligent systems.\"', '\"Machine learning aids in real-time customer insights.\"', '\"AI systems improve efficiency in supply chain management.\"', '\"Machine learning supports innovations in automated analytics.\"', '\"AI-driven platforms enhance personalization in digital services.\"', '\"Machine learning models assist in optimizing resource management.\"', '\"AI technologies contribute to advancements in computational models.\"', '\"Machine learning improves accuracy in medical diagnostics.\"', '\"AI systems enhance efficiency in customer service operations.\"', '\"Machine learning supports innovations in data-driven strategies.\"', '\"AI-driven solutions optimize business performance and efficiency.\"', '\"Machine learning models assist in real-time fraud detection.\"', '\"AI technologies contribute to advancements in virtual assistants.\"', '\"Machine learning improves accuracy in predictive analytics.\"', '\"AI systems enhance efficiency in research and development.\"', '\"Machine learning supports innovations in autonomous technology.\"', '\"AI-driven platforms enhance personalization in user experiences.\"', '\"Machine learning models assist in optimizing financial planning.\"', '\"AI technologies contribute to advancements in digital intelligence.\"', '\"Machine learning improves accuracy in language translation.\"', '\"AI systems enhance efficiency in energy management.\"', '\"Machine learning supports innovations in intelligent analytics.\"', '\"AI-driven solutions facilitate improvements in decision-making.\"', '\"Machine learning models assist in real-time business insights.\"', '\"AI technologies contribute to advancements in machine learning algorithms.\"', '\"Machine learning improves efficiency in data processing.\"', '\"AI systems enhance the accuracy of predictive models.\"', '\"Machine learning supports innovations in automated systems.\"', '\"AI-driven platforms enhance real-time data analysis.\"', '\"Machine learning models assist in optimizing marketing strategies.\"', '\"AI technologies contribute to advancements in intelligent automation.\"', '\"Machine learning improves accuracy in customer engagement.\"', '\"AI systems facilitate advancements in virtual and augmented reality.\"', '\"Machine learning supports innovations in predictive maintenance.\"', '\"AI-driven solutions enhance efficiency in digital workflows']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tokenization and Preprocessing data"
      ],
      "metadata": {
        "id": "uRObkg29rSrP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Tokenization is the process of breaking text into smaller units (words, sentences, etc.). Preprocessing involves cleaning and normalizing text by steps such as lowercasing, removing punctuation and stop words, and stemming or lemmatizing words. Together, these steps prepare text data for analysis or machine learning."
      ],
      "metadata": {
        "id": "Rwe_tiJmreN_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "13f0pnyxrgm-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the Tokenizer object to preprocess and vectorize text data\n",
        "tokenizer = Tokenizer()\n",
        "\n",
        "# Fit the Tokenizer on the list of sentences (or texts) to build the vocabulary\n",
        "tokenizer.fit_on_texts(flat_list)\n",
        "\n",
        "# Get the total number of unique words in the vocabulary, and assign and index value\n",
        "total_words = len(tokenizer.word_index) + 1\n",
        "\n",
        "# Print the total number of unique words in the vocabulary\n",
        "print(total_words)\n",
        "\n",
        "# Print the word index dictionary that maps words to their integer index\n",
        "print(tokenizer.word_index)\n",
        "\n",
        "\n",
        "# Initialize an empty list to hold the input sequences\n",
        "input_sequence = []\n",
        "\n",
        "# Iterate through each sentence in the list of sentences\n",
        "for line in flat_list:\n",
        "    # Convert each sentence into a sequence of integers using the tokenizer\n",
        "    # The 'texts_to_sequences' method converts words to their corresponding integer indices\n",
        "    token_list = tokener.texts_to_sequences([line])[0]\n",
        "    for i in range(1, len(token_list) + 1):  # Use len(token_list) + 1 to include the last element\n",
        "        n_gram_sequence = token_list[:i]\n",
        "        input_sequence.append(n_gram_sequence)\n",
        "\n",
        "# Find the maximum sequence length\n",
        "max_sequence_len = max(len(seq) for seq in input_sequence)\n",
        "\n",
        "# Pad sequences to\n",
        "padded_sequences = pad_sequences(input_sequence, maxlen=max_sequence_len, padding='pre')\n",
        "print(padded_sequences)\n",
        "\n",
        "# Summary:\n",
        "# This code initializes a Tokenizer, fits it on a list of sentences to build the vocabulary,\n",
        "# and then prints the total number of unique words and their corresponding indices in the word index."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6zpAnryErgY3",
        "outputId": "3bec3b92-8043-4473-bb54-c27d64dba878"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "284\n",
            "{'in': 1, 'machine': 2, 'learning': 3, 'ai': 4, 'systems': 5, 'models': 6, 'driven': 7, 'technologies': 8, 'enhance': 9, 'and': 10, 'advancements': 11, 'efficiency': 12, 'accuracy': 13, 'improves': 14, 'assist': 15, 'data': 16, 'innovations': 17, 'to': 18, 'optimizing': 19, 'support': 20, 'contribute': 21, 'supports': 22, 'of': 23, 'time': 24, 'management': 25, 'the': 26, 'real': 27, 'solutions': 28, 'platforms': 29, 'improve': 30, 'facilitate': 31, 'research': 32, 'aids': 33, 'analytics': 34, 'digital': 35, 'predictive': 36, 'financial': 37, 'customer': 38, 'analysis': 39, 'enhances': 40, 'decision': 41, 'making': 42, 'operations': 43, 'personalized': 44, 'business': 45, 'tools': 46, 'optimize': 47, 'autonomous': 48, 'strategies': 49, 'personalization': 50, 'intelligent': 51, 'algorithms': 52, 'applications': 53, 'user': 54, 'detection': 55, 'development': 56, 'marketing': 57, 'smart': 58, 'recommendations': 59, 'smarter': 60, 'supply': 61, 'virtual': 62, 'content': 63, 'advance': 64, 'resource': 65, 'processing': 66, 'intelligence': 67, 'insights': 68, 'processes': 69, 'experiences': 70, 'chain': 71, 'logistics': 72, 'energy': 73, 'fraud': 74, 'various': 75, 'capabilities': 76, 'with': 77, 'tasks': 78, 'helps': 79, 'technology': 80, 'detecting': 81, 'offer': 82, 'precision': 83, 'advanced': 84, 'maintenance': 85, 'language': 86, 'forecasting': 87, 'computer': 88, 'healthcare': 89, 'diagnostics': 90, 'human': 91, 'translation': 92, 'speech': 93, 'recognition': 94, 'service': 95, 'computational': 96, 'medical': 97, 'workflows': 98, 'automation': 99, 'artificial': 100, 'services': 101, 'effectiveness': 102, 'streamline': 103, 'visualization': 104, 'manufacturing': 105, 'planning': 106, 'interaction': 107, 'interfaces': 108, 'assistants': 109, 'robotics': 110, 'reality': 111, 'engagement': 112, 'industries': 113, 'devices': 114, 'automated': 115, 'innovation': 116, 'sectors': 117, 'new': 118, 'for': 119, 'techniques': 120, 'more': 121, 'fields': 122, 'patterns': 123, 'anomalies': 124, 'help': 125, 'natural': 126, 'assists': 127, 'science': 128, 'treatment': 129, 'threat': 130, 'e': 131, 'commerce': 132, 'vehicles': 133, 'behavior': 134, 'image': 135, 'buildings': 136, 'into': 137, 'market': 138, 'trends': 139, 'aid': 140, 'weather': 141, 'educational': 142, 'targeted': 143, 'experience': 144, 'allocation': 145, 'prevention': 146, 'automating': 147, 'creation': 148, 'relationship': 149, 'augmented': 150, 'home': 151, 'predictions': 152, 'sentiment': 153, 'transportation': 154, 'performance': 155, 'vision': 156, 'computing': 157, 'diagnostic': 158, 'production': 159, 'modeling': 160, 'recommendation': 161, 'operational': 162, 'improvements': 163, 'analyze': 164, 'vast': 165, 'amounts': 166, 'drive': 167, 'across': 168, 'different': 169, 'predict': 170, 'outcomes': 171, 'high': 172, 'can': 173, 'automate': 174, 'complex': 175, 'effectively': 176, 'ads': 177, 'are': 178, 'transforming': 179, 'how': 180, 'we': 181, 'interact': 182, 'through': 183, 'reshape': 184, 'traditional': 185, 'adapt': 186, 'learn': 187, 'from': 188, 'provide': 189, 'quick': 190, 'decisions': 191, 'over': 192, 'diverse': 193, 'automates': 194, 'routine': 195, 'understanding': 196, 'explores': 197, 'frontiers': 198, 'cybersecurity': 199, 'measures': 200, 'enables': 201, 'on': 202, 'accelerates': 203, 'drug': 204, 'discovery': 205, 'foster': 206, 'lead': 207, 'chatbots': 208, 'consumption': 209, 'deeper': 210, 'transactions': 211, 'climate': 212, 'predicting': 213, 'mitigating': 214, 'risks': 215, 'creativity': 216, 'design': 217, 'imaging': 218, 'enable': 219, 'effective': 220, 'traffic': 221, 'urban': 222, 'advertising': 223, 'gaming': 224, 'health': 225, 'advances': 226, 'resources': 227, 'criminal': 228, 'investigation': 229, 'agricultural': 230, 'practices': 231, 'medicine': 232, 'legal': 233, 'document': 234, 'search': 235, 'engines': 236, 'portfolio': 237, 'quality': 238, 'neuroscience': 239, 'brain': 240, 'studies': 241, 'genomics': 242, 'education': 243, 'administrative': 244, 'consumer': 245, 'chains': 246, 'telecommunication': 247, 'networks': 248, 'cities': 249, 'general': 250, 'drones': 251, 'campaigns': 252, 'agents': 253, 'streaming': 254, 'cyber': 255, 'threats': 256, 'inventory': 257, 'satisfaction': 258, 'facial': 259, 'wearable': 260, 'moderation': 261, 'agriculture': 262, 'infrastructure': 263, 'fitness': 264, 'wellness': 265, 'plans': 266, 'risk': 267, 'delivery': 268, 'quantum': 269, 'cognitive': 270, 'public': 271, 'administration': 272, 'robot': 273, 'security': 274, 'media': 275, 'large': 276, 'datasets': 277, 'efforts': 278, 'utilization': 279, 'text': 280, 'industrial': 281, 'interactions': 282, 'distribution': 283}\n",
            "[[ 0  0  0 ...  0  0  2]\n",
            " [ 0  0  0 ...  0  2  3]\n",
            " [ 0  0  0 ...  2  3 52]\n",
            " ...\n",
            " [ 0  0  0 ...  9 12  1]\n",
            " [ 0  0  0 ... 12  1 35]\n",
            " [ 0  0  4 ...  1 35 98]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " # Split the data into features (x) and targets (y)\n",
        "\n",
        "![05.02-samples-features.png](data:image/png;base64,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)\n",
        "\n"
      ],
      "metadata": {
        "id": "aSxzrSa1ENaM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (x) and targets (y)\n",
        "# Features (x) include all columns except the last one\n",
        "x_train = padded_sequences[:, :-1]\n",
        "\n",
        "# Targets (y) include only the last column\n",
        "y_train = padded_sequences[:, -1]\n",
        "\n",
        "# Print the features to verify their content\n",
        "print(\"Features (x):\")\n",
        "print(x)\n",
        "\n",
        "# Print the targets to verify their content\n",
        "print(\"Targets (y):\")\n",
        "print(y)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZlbszUnC-6f4",
        "outputId": "12174eab-80c0-4be8-af0c-8dc1ce435477"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features (x):\n",
            "[[ 0  0  0 ...  0  0  0]\n",
            " [ 0  0  0 ...  0  0  2]\n",
            " [ 0  0  0 ...  0  2  3]\n",
            " ...\n",
            " [ 0  0  0 ... 28  9 12]\n",
            " [ 0  0  0 ...  9 12  1]\n",
            " [ 0  0  4 ... 12  1 35]]\n",
            "Targets (y):\n",
            "[[0. 0. 1. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 1. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Convert the target data (y) to one-hot encoded format"
      ],
      "metadata": {
        "id": "4d-qCAPuEBNg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the target data (y) to one-hot encoded format\n",
        "# 'num_classes' specifies the total number of classes (e.g., total_words)\n",
        "y = tf.keras.utils.to_categorical(y_train, num_classes=total_words)\n",
        "\n",
        "# Print the one-hot encoded target data to verify the result\n",
        "print(\"One-hot encoded targets (y):\")\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C_kRPxPOBepL",
        "outputId": "8eb3b0a5-f915-460f-8cbd-ce69163db95c"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "One-hot encoded targets (y):\n",
            "[[0. 0. 1. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 1. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![1_ggtP4a5YaRx6l09KQaYOnw.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "FwZXdc7IDwyF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define Architecture RNN (Recurrent Neural Network)"
      ],
      "metadata": {
        "id": "mq0q-QqPFbcV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the model\n",
        "model = Sequential([\n",
        "    # Embedding layer: converts input sequences of integers into dense vectors of fixed size\n",
        "    Embedding(input_dim=total_words,  # Size of the vocabulary\n",
        "              output_dim=10,           # Dimension of the dense embedding vectors\n",
        "              input_length=max_sequence_len-1),  # Length of input sequences\n",
        "\n",
        "    # Simple RNN layer: processes the sequence data and retains the temporal information\n",
        "    SimpleRNN(10),  # Number of units in the RNN layer\n",
        "\n",
        "    # Dense layer: outputs the probability distribution over the vocabulary\n",
        "    Dense(total_words,            # Size of the output layer (same as vocabulary size)\n",
        "          activation='softmax')   # Softmax activation to produce probability distribution\n",
        "])\n"
      ],
      "metadata": {
        "id": "aovnAFd2Dyw8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bba672e3-a66e-479d-b9d1-f2106a7e02b5"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "_oKAngM-TOXZ",
        "outputId": "e8e29793-c098-4d7c-df2e-140d7d5528c5"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding_4 (\u001b[38;5;33mEmbedding\u001b[0m)              │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ simple_rnn_3 (\u001b[38;5;33mSimpleRNN\u001b[0m)             │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                      │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ embedding_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)              │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ simple_rnn_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SimpleRNN</span>)             │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(\n",
        "    optimizer='adam',           # Optimizer to use for training\n",
        "    loss='sparse_categorical_crossentropy',  # Loss function for multi-class classification\n",
        "    metrics=['accuracy']        # Metric to track during training and evaluation\n",
        ")"
      ],
      "metadata": {
        "id": "FIGgXjzAR1LR"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "history = model.fit(\n",
        "    x_train,                    # Input data\n",
        "    y_train,                    # Target labels\n",
        "    epochs=50,                  # Number of training epochs\n",
        "    batch_size=2,              # Number of samples per gradient update\n",
        "    validation_split=0.2,       # Fraction of data to be used for validation\n",
        "    verbose=1                   # Verbosity mode: 1 for progress bar\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qhRzQqd6SXZf",
        "outputId": "0dbaf16e-025d-498e-f0e7-9c73a3058e47"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 5ms/step - accuracy: 0.0819 - loss: 5.1535 - val_accuracy: 0.1791 - val_loss: 4.1278\n",
            "Epoch 2/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.1444 - loss: 4.3837 - val_accuracy: 0.1766 - val_loss: 3.8734\n",
            "Epoch 3/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.1669 - loss: 4.0994 - val_accuracy: 0.2413 - val_loss: 3.6625\n",
            "Epoch 4/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.2122 - loss: 3.9195 - val_accuracy: 0.2413 - val_loss: 3.4764\n",
            "Epoch 5/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.2359 - loss: 3.7199 - val_accuracy: 0.2637 - val_loss: 3.2327\n",
            "Epoch 6/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.2390 - loss: 3.5174 - val_accuracy: 0.2886 - val_loss: 3.0167\n",
            "Epoch 7/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.2577 - loss: 3.2925 - val_accuracy: 0.3358 - val_loss: 2.8689\n",
            "Epoch 8/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.2707 - loss: 3.1891 - val_accuracy: 0.3458 - val_loss: 2.7723\n",
            "Epoch 9/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.3110 - loss: 3.0514 - val_accuracy: 0.3408 - val_loss: 2.7046\n",
            "Epoch 10/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.3273 - loss: 3.0263 - val_accuracy: 0.3781 - val_loss: 2.6153\n",
            "Epoch 11/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.3552 - loss: 2.8472 - val_accuracy: 0.3557 - val_loss: 2.5670\n",
            "Epoch 12/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.3430 - loss: 2.8615 - val_accuracy: 0.3781 - val_loss: 2.4717\n",
            "Epoch 13/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.3526 - loss: 2.7907 - val_accuracy: 0.4279 - val_loss: 2.3975\n",
            "Epoch 14/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.3745 - loss: 2.6769 - val_accuracy: 0.4478 - val_loss: 2.3325\n",
            "Epoch 15/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.3786 - loss: 2.6351 - val_accuracy: 0.4154 - val_loss: 2.3079\n",
            "Epoch 16/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.3822 - loss: 2.5369 - val_accuracy: 0.4303 - val_loss: 2.2572\n",
            "Epoch 17/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.3904 - loss: 2.5119 - val_accuracy: 0.4478 - val_loss: 2.2231\n",
            "Epoch 18/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.4052 - loss: 2.4094 - val_accuracy: 0.4552 - val_loss: 2.1958\n",
            "Epoch 19/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.3899 - loss: 2.4222 - val_accuracy: 0.4552 - val_loss: 2.1691\n",
            "Epoch 20/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.3995 - loss: 2.3358 - val_accuracy: 0.4652 - val_loss: 2.1499\n",
            "Epoch 21/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.3913 - loss: 2.4541 - val_accuracy: 0.4552 - val_loss: 2.1400\n",
            "Epoch 22/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.4279 - loss: 2.2682 - val_accuracy: 0.4726 - val_loss: 2.1225\n",
            "Epoch 23/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.4166 - loss: 2.2616 - val_accuracy: 0.4652 - val_loss: 2.0972\n",
            "Epoch 24/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.4436 - loss: 2.2161 - val_accuracy: 0.4652 - val_loss: 2.0860\n",
            "Epoch 25/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.4091 - loss: 2.2052 - val_accuracy: 0.4677 - val_loss: 2.0735\n",
            "Epoch 26/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.4343 - loss: 2.1528 - val_accuracy: 0.4652 - val_loss: 2.0743\n",
            "Epoch 27/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.4145 - loss: 2.2009 - val_accuracy: 0.4627 - val_loss: 2.0551\n",
            "Epoch 28/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.4306 - loss: 2.1173 - val_accuracy: 0.4726 - val_loss: 2.0455\n",
            "Epoch 29/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.4317 - loss: 2.1068 - val_accuracy: 0.4652 - val_loss: 2.0329\n",
            "Epoch 30/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.4350 - loss: 2.1110 - val_accuracy: 0.4677 - val_loss: 2.0401\n",
            "Epoch 31/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.4468 - loss: 2.0355 - val_accuracy: 0.4801 - val_loss: 2.0219\n",
            "Epoch 32/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.4281 - loss: 2.0278 - val_accuracy: 0.4801 - val_loss: 2.0124\n",
            "Epoch 33/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.4498 - loss: 2.0049 - val_accuracy: 0.4826 - val_loss: 2.0097\n",
            "Epoch 34/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.4481 - loss: 2.0642 - val_accuracy: 0.4900 - val_loss: 1.9962\n",
            "Epoch 35/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.4661 - loss: 1.9276 - val_accuracy: 0.4851 - val_loss: 1.9929\n",
            "Epoch 36/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.4517 - loss: 1.9151 - val_accuracy: 0.4975 - val_loss: 1.9899\n",
            "Epoch 37/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - accuracy: 0.4679 - loss: 1.9420 - val_accuracy: 0.4876 - val_loss: 1.9821\n",
            "Epoch 38/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.4527 - loss: 1.9641 - val_accuracy: 0.4975 - val_loss: 1.9753\n",
            "Epoch 39/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.4709 - loss: 1.8876 - val_accuracy: 0.4950 - val_loss: 1.9615\n",
            "Epoch 40/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.4644 - loss: 1.8942 - val_accuracy: 0.5000 - val_loss: 1.9695\n",
            "Epoch 41/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.4470 - loss: 1.9571 - val_accuracy: 0.4900 - val_loss: 1.9650\n",
            "Epoch 42/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.4613 - loss: 1.8304 - val_accuracy: 0.5025 - val_loss: 1.9573\n",
            "Epoch 43/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.4647 - loss: 1.8431 - val_accuracy: 0.4826 - val_loss: 1.9554\n",
            "Epoch 44/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.4573 - loss: 1.8776 - val_accuracy: 0.4826 - val_loss: 1.9474\n",
            "Epoch 45/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.4548 - loss: 1.8218 - val_accuracy: 0.5025 - val_loss: 1.9490\n",
            "Epoch 46/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.4742 - loss: 1.7794 - val_accuracy: 0.5050 - val_loss: 1.9506\n",
            "Epoch 47/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.4895 - loss: 1.7715 - val_accuracy: 0.5025 - val_loss: 1.9386\n",
            "Epoch 48/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.4714 - loss: 1.7657 - val_accuracy: 0.5075 - val_loss: 1.9402\n",
            "Epoch 49/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.5059 - loss: 1.6911 - val_accuracy: 0.5025 - val_loss: 1.9446\n",
            "Epoch 50/50\n",
            "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.4534 - loss: 1.7997 - val_accuracy: 0.5100 - val_loss: 1.9391\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This function predicts the next word(s) given a starting piece of text (seed_text).\n",
        "def predict_next_word(seed_text, next_words=1):\n",
        "    # Loop to predict the next word 'next_words' times\n",
        "    for _ in range(next_words):\n",
        "        # Convert the seed text into a format that the model can understand (a sequence of numbers)\n",
        "        token_list = tokenizer.texts_to_sequences([seed_text])\n",
        "\n",
        "        # Ensure the sequence is the right length for the model by padding it with zeros if needed\n",
        "        token_list = pad_sequences(token_list, maxlen=max_sequence_len, padding='pre')\n",
        "\n",
        "        # Use the model to predict the probabilities of the next word\n",
        "        predicted_probs = model.predict(token_list, verbose=0)\n",
        "        # Find the index of the word with the highest probability\n",
        "        predicted_index = np.argmax(predicted_probs, axis=-1)[0]\n",
        "\n",
        "        # Convert the index back into the actual word\n",
        "        predicted_word = tokenizer.index_word.get(predicted_index, '')\n",
        "\n",
        "        # Add the predicted word to the end of the seed text\n",
        "        seed_text += ' ' + predicted_word\n",
        "\n",
        "    # Return the updated seed text with the predicted words added\n",
        "    return seed_text\n",
        "\n",
        "# Example usage of the function:\n",
        "seed_text = \"what is ai \"\n",
        "# Predict the next 4 words based on the seed text\n",
        "predicted_text = predict_next_word(seed_text, next_words=50)\n",
        "# Print the result\n",
        "print(predicted_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-bbfqPVWAis",
        "outputId": "795a6e29-9d16-47ce-b45e-24aadbcd7986"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "what is ai  technologies contribute to advancements in data driven processes devices ai learning models enhance accuracy in financial forecasting and interaction interfaces technology ai learning models enhance accuracy in various industries effectively ai technologies contribute to advancements in data driven processes devices ai learning models enhance accuracy in financial forecasting and interaction\n"
          ]
        }
      ]
    }
  ]
}