embedding_layer_implementaiton.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "private_outputs": true,
      "provenance": [],
      "authorship_tag": "ABX9TyNMw7/oi7M3v0/0zNOeIwXt",
      "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/arya2910/Text-to-Text-generation/blob/main/embedding_layer_implementaiton.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install Keras-Preprocessing"
      ],
      "metadata": {
        "id": "gbRHxCQVIaSs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DvE5wim85uzG"
      },
      "outputs": [],
      "source": [
        "from numpy import array\n",
        "from keras.preprocessing.text import one_hot\n",
        "from keras_preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Embedding"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "docs = ['Well done!',\n",
        "\t\t'Good work',\n",
        "\t\t'Great effort',\n",
        "\t\t'nice work',\n",
        "\t\t'Excellent!',\n",
        "\t\t'Weak',\n",
        "\t\t'Poor effort!',\n",
        "\t\t'not good',\n",
        "\t\t'poor work',\n",
        "\t\t'Could have done better.']"
      ],
      "metadata": {
        "id": "O1mKzjj_AEBG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "voc_size= 10000\n",
        "one_hot_represent= [one_hot(i, voc_size) for i in docs]\n",
        "print(one_hot_represent)"
      ],
      "metadata": {
        "id": "5hTfMoGAIjfL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 4\n",
        "padded_docs = pad_sequences(one_hot_represent, maxlen=max_length, padding='post')\n",
        "print(padded_docs)"
      ],
      "metadata": {
        "id": "aQ7VAYkOJEZK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(voc_size, 10, input_length=max_length))\n",
        "model.compile('adam', 'mse')"
      ],
      "metadata": {
        "id": "nHpdbU9nJRmZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.summary)"
      ],
      "metadata": {
        "id": "7abmWCwTJ0h4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(padded_docs)"
      ],
      "metadata": {
        "id": "m16AV45bJ61J"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}