TF2_0_Test_Preprocessing.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPm8ebSdALk8TRQz4+yQabj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sreya-rc/TF2.0/blob/main/TF2_0_Test_Preprocessing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mxlbc9cAM4EO",
        "outputId": "14c5cfc6-9459-4c3a-e96f-1688e2d5f965"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n",
            "2.12.0\n"
          ]
        }
      ],
      "source": [
        "# Install TensorFlow\n",
        "try:\n",
        "  %tensorflow_version 2.x # Colab only.\n",
        "except Exception:\n",
        "  pass\n",
        "\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "from tensorflow.keras.layers import Input, Dense, SimpleRNN, GRU, LSTM, Flatten, GlobalMaxPooling1D\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import SGD, Adam\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "0darT2QENGmT"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentences = [\n",
        "    \"I like eggs and ham.\",\n",
        "    \"I love chocolate and bunnies.\",\n",
        "    \"I hate onions.\"\n",
        "]"
      ],
      "metadata": {
        "id": "pDUw0eKXNgXp"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MAX_VOCAB_SIZE = 20000\n",
        "tokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE)\n",
        "tokenizer.fit_on_texts(sentences)\n",
        "sequences = tokenizer.texts_to_sequences(sentences)"
      ],
      "metadata": {
        "id": "wptEjRLsPbfm"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(sequences)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "067t0YDsQTCK",
        "outputId": "ac0f1999-4875-4309-a8dd-0a1654e2a3ff"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1, 3, 4, 2, 5], [1, 6, 7, 2, 8], [1, 9, 10]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get word to index mapping\n",
        "tokenizer.word_index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WyLEyM2vQVfk",
        "outputId": "3ed294b5-8b36-4f67-c3d7-fb1a90393ab5"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'i': 1,\n",
              " 'and': 2,\n",
              " 'like': 3,\n",
              " 'eggs': 4,\n",
              " 'ham': 5,\n",
              " 'love': 6,\n",
              " 'chocolate': 7,\n",
              " 'bunnies': 8,\n",
              " 'hate': 9,\n",
              " 'onions': 10}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the defaults\n",
        "data = pad_sequences(sequences)\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49BAUoWeQeZ3",
        "outputId": "89ca8a9c-f543-4f9a-c723-1996e8fa9eaa"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1  3  4  2  5]\n",
            " [ 1  6  7  2  8]\n",
            " [ 0  0  1  9 10]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Another moethod for padding\n",
        "MAX_LEN = 5\n",
        "data = pad_sequences(sequences, maxlen=MAX_LEN)\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yiLQz4KIQ0tv",
        "outputId": "0df08ed2-e79a-4349-80b7-5b55a31e676d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1  3  4  2  5]\n",
            " [ 1  6  7  2  8]\n",
            " [ 0  0  1  9 10]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pad_sequences(sequences, maxlen=MAX_LEN, padding='post')\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1xG3iYzQRG93",
        "outputId": "a63f1c59-4760-4047-a7a6-a79b86a8c337"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1  3  4  2  5]\n",
            " [ 1  6  7  2  8]\n",
            " [ 1  9 10  0  0]]\n"
          ]
        }
      ]
    }
  ]
}