word_encoding_using_keras.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "5i0p7EWM91un"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "samples = ['The cat sat on the mat.', 'The dog ate my homework.']"
      ],
      "metadata": {
        "id": "mNbcvSlN99c5"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#remove collision \n",
        "\n",
        "results = np.zeros((len(samples), 5, 20))\n",
        "for i, sample in enumerate(samples):\n",
        "  for j, word in list(enumerate(sample.split()))[:5]:\n",
        "    index = abs(hash(word)) % 20\n",
        "    results[i, j, index] = 1."
      ],
      "metadata": {
        "id": "d3kFX479-l6f"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p1kC2ndZ-3RP"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mAo95R0b-5iP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}