{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "df1f5eb3",
   "metadata": {},
   "source": [
    "# demonstration of broadcasting in tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d61c70a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38bca1cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define two tensors with different shapes\n",
    "a = tf.constant([[1, 2, 3], [4, 5, 6]])\n",
    "b = tf.constant([10, 20, 30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3f382e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform element-wise multiplication using broadcasting\n",
    "c = a * b\n",
    "# Print the result\n",
    "print(c)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95683fe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Broadcasting scalar to tensor\n",
    "x = tf.constant([1, 2, 3])\n",
    "y = 2\n",
    "z = x + y  # equivalent to tf.add(x, y)\n",
    "print(z.numpy())  # [3 4 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ed98565",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Broadcasting vector to matrix\n",
    "x = tf.constant([[1, 2], [3, 4]])\n",
    "y = tf.constant([1, 2])\n",
    "z = x + y  # equivalent to tf.add(x, y)\n",
    "print(z.numpy())  # [[2 4], [4 6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41f4196f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Broadcasting matrix to tensor\n",
    "x = tf.constant([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n",
    "y = tf.constant([[1], [2]])\n",
    "z = x + y  # equivalent to tf.add(x, y)\n",
    "print(z.numpy())  # [[[2 3], [4 5]], [[7 8], [9 10]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76a5108d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
