{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eefe7571",
   "metadata": {},
   "outputs": [],
   "source": [
    "# show differentiation in Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9d7c185",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "584384f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to differentiate\n",
    "def f(x):\n",
    "    return x ** 2 + 2 * x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70430402",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a TensorFlow variable\n",
    "x = tf.Variable(2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45ea0a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use tf.GradientTape to record the gradients\n",
    "with tf.GradientTape() as tape:\n",
    "    y = f(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6b1ff27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the gradient of y with respect to x\n",
    "dy_dx = tape.gradient(y, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f581817",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print the result\n",
    "print(dy_dx)"
   ]
  }
 ],
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