tensorflow_introduction.ipynb

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  "metadata": {
    "colab": {
      "name": "tensorflow-introduction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOIhRQ0cQO2u6Dj4hC6aSqL",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/brycemcwilliams/research-papers/blob/master/tensorflow_introduction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kMYq9GGsMnZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1349536b-dfb0-4cae-9f77-48c441720559"
      },
      "source": [
        "!pip install tensorflow==2.0.0"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/46/0f/7bd55361168bb32796b360ad15a25de6966c9c1beb58a8e30c01c8279862/tensorflow-2.0.0-cp36-cp36m-manylinux2010_x86_64.whl (86.3MB)\n",
            "\u001b[K     |████████████████████████████████| 86.3MB 75kB/s \n",
            "\u001b[?25hRequirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (3.2.1)\n",
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            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.1.2)\n",
            "Collecting tensorflow-estimator<2.1.0,>=2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fc/08/8b927337b7019c374719145d1dceba21a8bb909b93b1ad6f8fb7d22c1ca1/tensorflow_estimator-2.0.1-py2.py3-none-any.whl (449kB)\n",
            "\u001b[K     |████████████████████████████████| 450kB 27.7MB/s \n",
            "\u001b[?25hCollecting gast==0.2.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.12.1)\n",
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            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (0.9.0)\n",
            "Collecting tensorboard<2.1.0,>=2.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/76/54/99b9d5d52d5cb732f099baaaf7740403e83fe6b0cedde940fabd2b13d75a/tensorboard-2.0.2-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8MB 39.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.18.4)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.0.0) (1.29.0)\n",
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            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.0.1)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (2.23.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.2.2)\n",
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            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (46.4.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.4.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (2.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (2020.4.5.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.24.3)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.6.0)\n",
            "Requirement already satisfied: rsa<4.1,>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (4.0)\n",
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            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (1.3.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.1.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<4.1,>=3.1.4->google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0) (3.1.0)\n",
            "Building wheels for collected packages: gast\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=2f96c5067f7c61bff2010312847dd2e8c8ba37e0f0c7672f15bddbf3502bdd87\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "Successfully built gast\n",
            "\u001b[31mERROR: tensorflow-probability 0.10.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tensorflow-estimator, gast, tensorboard, tensorflow\n",
            "  Found existing installation: tensorflow-estimator 2.2.0\n",
            "    Uninstalling tensorflow-estimator-2.2.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.2.0\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "  Found existing installation: tensorboard 2.2.1\n",
            "    Uninstalling tensorboard-2.2.1:\n",
            "      Successfully uninstalled tensorboard-2.2.1\n",
            "  Found existing installation: tensorflow 2.2.0\n",
            "    Uninstalling tensorflow-2.2.0:\n",
            "      Successfully uninstalled tensorflow-2.2.0\n",
            "Successfully installed gast-0.2.2 tensorboard-2.0.2 tensorflow-2.0.0 tensorflow-estimator-2.0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFuM7vUCsO0a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6e8efd6a-a604-46b1-8b8b-4e5584c6dfb9"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TesrvQLisiZQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "8efd4a66-437b-4fb5-c5b3-d5465cd44cf4"
      },
      "source": [
        "a = 4 + 2j # complex number\n",
        "b = 6 + 7j\n",
        "\n",
        "c = a + b\n",
        "d = tf.square(c)\n",
        "print(a,b,c,d)\n",
        "\n",
        "print(a-b)\n",
        "print(a*b)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4+2j) (6+7j) (10+9j) tf.Tensor((19+180j), shape=(), dtype=complex128)\n",
            "(-2-5j)\n",
            "(10+40j)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIKZyfVss0GG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "80929088-b834-4ed7-ffe7-77b392584e2b"
      },
      "source": [
        "c = tf.constant([[1.4, 2.0, 2], [8, 2, 5]])\n",
        "print(c)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[1.4 2.  2. ]\n",
            " [8.  2.  5. ]], shape=(2, 3), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhb-lJXstMt1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "96a93f1c-586e-4e16-b6e8-c6692d25b91a"
      },
      "source": [
        "import numpy as np\n",
        "y = np.array([[1, 2, 2], [8, 2, 5]])\n",
        "\n",
        "print(y)\n",
        "# We can also convert a tensor into a numpy array by using .numpy()\n",
        "c.numpy()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1 2 2]\n",
            " [8 2 5]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.4, 2. , 2. ],\n",
              "       [8. , 2. , 5. ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bcz5P_JZtVrW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "983b292d-1694-406a-d384-439a9162ed07"
      },
      "source": [
        "print('Tensor Data Type', c.dtype)\n",
        "print('Tensor Shape', c.shape)\n",
        "# You can notice the similarity with numpy \n",
        "# You can also call the different default tensors as you would do for numpy"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor Data Type <dtype: 'float32'>\n",
            "Tensor Shape (2, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "En7sWQj7tld8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "b6e5b684-837f-4fba-beb6-d0bf1c9fe20f"
      },
      "source": [
        "c"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=4, shape=(2, 3), dtype=float32, numpy=\n",
              "array([[1.4, 2. , 2. ],\n",
              "       [8. , 2. , 5. ]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L05x8vcEtn_O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9a7808c4-6db2-4c17-b5d3-7bb1018adf6c"
      },
      "source": [
        "c.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([2, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6ng8gt6tr9t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "7d18eb02-49da-4e74-b9a2-ae4c0be11d34"
      },
      "source": [
        "tf.ones([3, 3], tf.int32)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=10, shape=(3, 3), dtype=int32, numpy=\n",
              "array([[1, 1, 1],\n",
              "       [1, 1, 1],\n",
              "       [1, 1, 1]], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdaP5Z5EuUFD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "260ec886-7371-4773-bc5e-7a1546e7eb99"
      },
      "source": [
        "tf.zeros([3, 3], tf.int32)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=13, shape=(3, 3), dtype=int32, numpy=\n",
              "array([[0, 0, 0],\n",
              "       [0, 0, 0],\n",
              "       [0, 0, 0]], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RaQS2t7SubYr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "de08e7eb-8114-49a0-94f3-085fa1a788c8"
      },
      "source": [
        "random_variable = tf.ones(shape = (3,3))\n",
        "# random variable\n",
        "tf_variable = tf.Variable(random_variable)\n",
        "print(tf_variable)\n",
        "\n",
        "# you can also update the values of your variable.\n",
        "tf_variable.assign(tf.zeros(shape = (3,3)))\n",
        "print(tf_variable)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tf.Variable 'Variable:0' shape=(3, 3) dtype=float32, numpy=\n",
            "array([[1., 1., 1.],\n",
            "       [1., 1., 1.],\n",
            "       [1., 1., 1.]], dtype=float32)>\n",
            "<tf.Variable 'Variable:0' shape=(3, 3) dtype=float32, numpy=\n",
            "array([[0., 0., 0.],\n",
            "       [0., 0., 0.],\n",
            "       [0., 0., 0.]], dtype=float32)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQVpmPIC1ZSQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "4294c978-7f6f-4af5-eb2f-76ad03ecfcef"
      },
      "source": [
        "# computing derivative for the function 3x^2 at x = 3\n",
        "\n",
        "x = tf.constant(3.0) # gradient at\n",
        "with tf.GradientTape() as g:\n",
        "  g.watch(x) # record the operations\n",
        "  y = 3*x*x\n",
        "dy_dx = g.gradient(y, x) \n",
        "print(dy_dx)\n",
        "\n",
        "\n",
        "# you can also use nested GradientTape() for the second derivative\n",
        "# computing second derivative for the function 4x^2 at x = 4.0\n",
        "\n",
        "x = tf.constant(4.0)\n",
        "with tf.GradientTape() as gt:\n",
        "  gt.watch(x)\n",
        "  with tf.GradientTape() as g:\n",
        "    g.watch(x)  \n",
        "    y = 4*x*x\n",
        "  dy_dx = g.gradient(y, x)\n",
        "d2y_dx2 = gt.gradient(dy_dx,x)\n",
        "print(d2y_dx2)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(18.0, shape=(), dtype=float32)\n",
            "tf.Tensor(8.0, shape=(), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-fEb1Po2H1T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "3df3c89a-0b7f-434b-bc21-fdf3a1ccf4e2"
      },
      "source": [
        "x = tf.constant(1.0, shape=[3, 3])\n",
        "print(x)\n",
        "exp = tf.math.exp(x)\n",
        "print(exp)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[1. 1. 1.]\n",
            " [1. 1. 1.]\n",
            " [1. 1. 1.]], shape=(3, 3), dtype=float32)\n",
            "tf.Tensor(\n",
            "[[2.7182817 2.7182817 2.7182817]\n",
            " [2.7182817 2.7182817 2.7182817]\n",
            " [2.7182817 2.7182817 2.7182817]], shape=(3, 3), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5wTeP2H4h-J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "06d89320-5194-439e-935a-ffd1eb91839a"
      },
      "source": [
        "var = 2j\n",
        "print(var)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2j\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kcdKuw14wT7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "dcd1e327-9d5e-4937-ac6e-feb46f2d226f"
      },
      "source": [
        "x = tf.Variable(tf.random.normal(shape=(3,2)))\n",
        "x"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable:0' shape=(3, 2) dtype=float32, numpy=\n",
              "array([[-1.6141663,  0.9079517],\n",
              "       [ 0.5893458,  0.9616784],\n",
              "       [ 1.9611958, -0.9466995]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n9eFuVQOJCVw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "54e6d0f3-5d03-4950-a047-9d1fb36b359c"
      },
      "source": [
        "x"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable:0' shape=(3, 2) dtype=float32, numpy=\n",
              "array([[-1.6141663,  0.9079517],\n",
              "       [ 0.5893458,  0.9616784],\n",
              "       [ 1.9611958, -0.9466995]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85jcgrC8JDoY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "dbe798ba-49e2-4722-dd29-396746e40b69"
      },
      "source": [
        "# your code here\n",
        "x.assign_sub(tf.ones(shape = (3,2)))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'UnreadVariable' shape=(3, 2) dtype=float32, numpy=\n",
              "array([[-2.6141663 , -0.09204829],\n",
              "       [-0.4106542 , -0.03832161],\n",
              "       [ 0.9611958 , -1.9466995 ]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6IMooGdJI6A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "40f8faf6-352f-4913-f2aa-dbf8dec0e552"
      },
      "source": [
        "# your code here\n",
        "x = tf.constant(1.0) \n",
        "with tf.GradientTape() as g:\n",
        "  g.watch(x) \n",
        "  with tf.GradientTape() as g1:\n",
        "    g1.watch(x) \n",
        "    with tf.GradientTape() as g2:\n",
        "      g2.watch(x) \n",
        "      y = 4*x*x*x + x*x + 1\n",
        "    dy_dx = g2.gradient(y, x)   \n",
        "  d2y_dx2 = g1.gradient(dy_dx, x)\n",
        "d3y_dx3 = g.gradient(d2y_dx2, x)\n",
        "\n",
        "print(d3y_dx3)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(24.0, shape=(), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0OOB9OT0NeNp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "b97674bd-7cf0-4f89-87b6-e532d1c372e5"
      },
      "source": [
        "# your code here for dot product\n",
        "a = tf.constant([[1,2,3],[4,5,6],[7,8,9]])\n",
        "b = tf.constant([[1],[2],[3]])\n",
        "\n",
        "c = tf.matmul(a, b)\n",
        "print(c)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[14]\n",
            " [32]\n",
            " [50]], shape=(3, 1), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RT9TVQtJOYKq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "a4499ef6-5ef8-4ad1-c5fc-ad6b8ae73b17"
      },
      "source": [
        "# your code here for element wise product\n",
        "d = tf.multiply(a, b)\n",
        "d"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=149, shape=(3, 3), dtype=int32, numpy=\n",
              "array([[ 1,  2,  3],\n",
              "       [ 8, 10, 12],\n",
              "       [21, 24, 27]], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xu5ABDY8OlGi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}