TensorFlow/TensorFlow_2_0_Introduction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TensorFlow 2.0 Introduction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqnQhKafgyZC"
      },
      "source": [
        "## TensorFlow 1.X"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SiauG1locQIa"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "### Installing TensorFlow 1.X\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVM4JRo8cJ0e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb8875f0-17ce-49ec-cffc-4ab4f78e44c8"
      },
      "source": [
        "%tensorflow_version 1.x"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQcq7zJ4cgf_"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKzynmqddNBO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fa352ebe-a8e3-48f6-c943-717f1320dfd7"
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.15.2'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kyvi9fbgfNXO"
      },
      "source": [
        "### Constants"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSSEoeadsbn5"
      },
      "source": [
        "#### Defining a constant"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCnWxoK3e1rm"
      },
      "source": [
        "# Defining a TensorFlow constant\n",
        "tensor = tf.constant([[23, 4], [32, 51]])"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6YBDabufXO1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee2e90c0-7850-4868-9ff3-ff46b70aea69"
      },
      "source": [
        "tensor"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'Const:0' shape=(2, 2) dtype=int32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdNftutxfZ5N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "62dd3bdd-5958-4065-a6cb-719ceb48f26f"
      },
      "source": [
        "# If a session is not initialized, we cannot access the values of the constant\n",
        "tensor.eval()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-85663888a91e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# If a session is not initialized, we cannot access the values of the constant\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, feed_dict, session)\u001b[0m\n\u001b[1;32m    796\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    797\u001b[0m     \"\"\"\n\u001b[0;32m--> 798\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_eval_using_default_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    799\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    800\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mexperimental_ref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36m_eval_using_default_session\u001b[0;34m(tensors, feed_dict, graph, session)\u001b[0m\n\u001b[1;32m   5391\u001b[0m     \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_default_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5392\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msession\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5393\u001b[0;31m       raise ValueError(\"Cannot evaluate tensor using `eval()`: No default \"\n\u001b[0m\u001b[1;32m   5394\u001b[0m                        \u001b[0;34m\"session is registered. Use `with \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5395\u001b[0m                        \u001b[0;34m\"sess.as_default()` or pass an explicit session to \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Cannot evaluate tensor using `eval()`: No default session is registered. Use `with sess.as_default()` or pass an explicit session to `eval(session=sess)`"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E-VQJUFxf9fU"
      },
      "source": [
        "#### Getting the values of a constant"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlHowT2SgDmt"
      },
      "source": [
        "session = tf.Session()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iM9jQMJhgGzi"
      },
      "source": [
        "session.run(tf.global_variables_initializer())"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xk-Ng1_0fmeV"
      },
      "source": [
        "tensor_value = session.run(tensor)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4c_EfA0zgBb9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19a836da-1b3e-4a80-98f9-ade8da7f9019"
      },
      "source": [
        "tensor_value"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[23,  4],\n",
              "       [32, 51]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UAE50vlcdasP"
      },
      "source": [
        "### Variables"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-C8yhSZksm1R"
      },
      "source": [
        "#### Defining a variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tex4Ud2JdOe9"
      },
      "source": [
        "# Defining a TensorFlow variable\n",
        "variable = tf.Variable([[30, 20], [10, 45]])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMco0fladtkt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6af6f5a-de28-468c-e08b-1e046901ee48"
      },
      "source": [
        "variable"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable:0' shape=(2, 2) dtype=int32_ref>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OAZRkgwed9X-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "ce027320-75b6-49a2-f6de-f0e7c5ca69eb"
      },
      "source": [
        "# If a session is not initialized, we cannot access the values of the variable\n",
        "variable.eval()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-4ca841f360bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# If a session is not initialized, we cannot access the values of the variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/ops/variables.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, session)\u001b[0m\n\u001b[1;32m   2022\u001b[0m       \u001b[0mA\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0ma\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2023\u001b[0m     \"\"\"\n\u001b[0;32m-> 2024\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2026\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, feed_dict, session)\u001b[0m\n\u001b[1;32m    796\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    797\u001b[0m     \"\"\"\n\u001b[0;32m--> 798\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_eval_using_default_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    799\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    800\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mexperimental_ref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36m_eval_using_default_session\u001b[0;34m(tensors, feed_dict, graph, session)\u001b[0m\n\u001b[1;32m   5391\u001b[0m     \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_default_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5392\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msession\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5393\u001b[0;31m       raise ValueError(\"Cannot evaluate tensor using `eval()`: No default \"\n\u001b[0m\u001b[1;32m   5394\u001b[0m                        \u001b[0;34m\"session is registered. Use `with \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5395\u001b[0m                        \u001b[0;34m\"sess.as_default()` or pass an explicit session to \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Cannot evaluate tensor using `eval()`: No default session is registered. Use `with sess.as_default()` or pass an explicit session to `eval(session=sess)`"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lsguZm4gep9g"
      },
      "source": [
        "#### Getting the values of a variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Nwg0W4LedDW"
      },
      "source": [
        "# Initializing a session\n",
        "session = tf.Session()"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UnPhvNt1eu_e"
      },
      "source": [
        "# Initializing ALL the variables from the session\n",
        "session.run(tf.global_variables_initializer())"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yMmNCDviex--",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1039a4e-d7b7-41b7-8d4e-0e3d33f6b0ec"
      },
      "source": [
        "# Running the eval method on the environment with the initialized session to retrieve values from it\n",
        "variable.eval(session)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[30, 20],\n",
              "       [10, 45]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJ6s9hH5gsvW"
      },
      "source": [
        "## TensorFlow 2.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zl7ADLJ_g38A"
      },
      "source": [
        "### Installing TensorFlow 2.0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrAiBgVg_WtL"
      },
      "source": [
        "%tensorflow_version 2.x"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsQVt3TghDWN"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjZ7TmxNh3FE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "00a85b64-b91a-4a48-e8e1-826971ecc294"
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.4.1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L3jqjCppiB0x"
      },
      "source": [
        "### Constants"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F58hfumftBPK"
      },
      "source": [
        "#### Defining a constant"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EL_NS0EDh4U2"
      },
      "source": [
        "# Defining a constant in TensorFlow 2.0\n",
        "tensor_20 = tf.constant([[23, 4], [32, 51]])"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zu1SK9N-iLj-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28c440ac-5547-4cdd-972d-86e08af0b89e"
      },
      "source": [
        "tensor_20"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=int32, numpy=\n",
              "array([[23,  4],\n",
              "       [32, 51]], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pi1hQ7VYiNoP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ea47760-ec5a-47df-b999-b1237b931da6"
      },
      "source": [
        "# Getting the shape of a tensor\n",
        "tensor_20.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KfR8I7EOituB"
      },
      "source": [
        "#### Getting the values of a constant"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C1mt_C-7iV3l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7503a11-2d01-41a1-bd7a-ba1ca379788b"
      },
      "source": [
        "# Getting the values straight from a TensorFlow constant with numpy, and without the need of a session\n",
        "tensor_20.numpy()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[23,  4],\n",
              "       [32, 51]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4yu4Rvo2igb2"
      },
      "source": [
        "# We are able to convert a numpy array back to a TensorFlow tensor as well\n",
        "numpy_tensor = np.array([[23,  4], [32, 51]])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uLZ7XAYjOO2"
      },
      "source": [
        "tensor_from_numpy = tf.constant(numpy_tensor)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltsz-6FrjWDE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba498e1a-af15-49af-a98b-ff2745f14f07"
      },
      "source": [
        "tensor_from_numpy"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=int64, numpy=\n",
              "array([[23,  4],\n",
              "       [32, 51]])>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uq-atYpaV2mB"
      },
      "source": [
        "### Variables"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzoCDLwlt8V-"
      },
      "source": [
        "#### Defining a variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INUlB5zoIB90",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23918143-5ee3-418d-815a-71133b0039c7"
      },
      "source": [
        "tf2_variable = tf.Variable([[1., 2., 3.], [4., 5., 6.]])\n",
        "tf2_variable"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable:0' shape=(2, 3) dtype=float32, numpy=\n",
              "array([[1., 2., 3.],\n",
              "       [4., 5., 6.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oqi-QOsMWo7F"
      },
      "source": [
        "#### Getting the values of a variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCQr70r1IhLF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee750933-235d-4590-c639-555a7ce144d4"
      },
      "source": [
        "tf2_variable.numpy()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 2., 3.],\n",
              "       [4., 5., 6.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ka8Tpnk_WvET"
      },
      "source": [
        "#### Changing a specific value in the variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23OsvfbXWtel",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b14a58e-ac8b-4de1-a381-bf2587600bf3"
      },
      "source": [
        "tf2_variable[0, 2].assign(100)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'UnreadVariable' shape=(2, 3) dtype=float32, numpy=\n",
              "array([[  1.,   2., 100.],\n",
              "       [  4.,   5.,   6.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cSj0YvJkXZu8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30c3451b-f30e-479b-e33d-49ed7f7ebbb5"
      },
      "source": [
        "tf2_variable"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable:0' shape=(2, 3) dtype=float32, numpy=\n",
              "array([[  1.,   2., 100.],\n",
              "       [  4.,   5.,   6.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QdbG1kvXD2O-"
      },
      "source": [
        "### Operations with tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8TE6VvuFDXd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9416fb57-7d40-461e-a14e-03d36cda43b0"
      },
      "source": [
        "tensor = tf.constant([[1, 2], [3, 4]])\n",
        "tensor"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=int32, numpy=\n",
              "array([[1, 2],\n",
              "       [3, 4]], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9AelsMJIFYku"
      },
      "source": [
        "#### Addition between a scalar and a tensor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBHn9DTVjW6s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce4c79bf-5184-4eba-e6b0-135623eaec21"
      },
      "source": [
        "tensor + 2"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=int32, numpy=\n",
              "array([[3, 4],\n",
              "       [5, 6]], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQY7rduwFfEC"
      },
      "source": [
        "#### Multiplication between a scalar and a tensor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZkzLDnVFBZF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b85876c-4946-4c54-cc5c-726601682cca"
      },
      "source": [
        "tensor * 5"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(2, 2), dtype=int32, numpy=\n",
              "array([[ 5, 10],\n",
              "       [15, 20]], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0algGv7HFrUI"
      },
      "source": [
        "#### Using Numpy functions on TensorFlow tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTeiyc6HFyIs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "125f37a9-4630-411f-e639-03176410e82a"
      },
      "source": [
        "# Getting the squares of all numbers in a TensorFlow tensor object\n",
        "np.square(tensor)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  4],\n",
              "       [ 9, 16]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zcR_Z_4vF2wG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22c0486a-a739-414b-c471-6966c2696bc7"
      },
      "source": [
        "# Getting the square root of all numbers in a tensorflow tensor object\n",
        "np.sqrt(tensor)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 1.41421356],\n",
              "       [1.73205081, 2.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UL--f1anIMRl"
      },
      "source": [
        "#### Dot product between two tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6DGgNB--F6Q1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cea5b6d-e447-4ac5-ff04-d54eb8a87d4e"
      },
      "source": [
        " np.dot(tensor, tensor_20)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 87, 106],\n",
              "       [197, 216]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12dtWEKjXl7l"
      },
      "source": [
        "### Strings in TensorFlow 2.0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fXj_Be2bXgdj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da9eb557-e2e9-4ef8-843a-10675d2418a3"
      },
      "source": [
        "tf_string = tf.constant(\"TensorFlow\")\n",
        "tf_string"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=string, numpy=b'TensorFlow'>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9VXL59XYPDG"
      },
      "source": [
        "#### Simple string operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sIYqcC9YjoS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c67e8f03-2163-4e85-ce9e-e38a0d248102"
      },
      "source": [
        "tf.strings.length(tf_string)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=int32, numpy=10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxQRhILBYmiH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a347f66-b004-49f2-c272-3a41f234c8d5"
      },
      "source": [
        "tf.strings.unicode_decode(tf_string, \"UTF8\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=int32, numpy=array([ 84, 101, 110, 115, 111, 114,  70, 108, 111, 119], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tEjJCCK2ZUzT"
      },
      "source": [
        "#### Storing arrays of strings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YHvHvS0DZakV"
      },
      "source": [
        "tf_string_array = tf.constant([\"TensorFlow\", \"Deep Learning\", \"AI\"])"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyFS4E9uZh21",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "528bbca4-445a-4d9f-b775-a74cae37ca63"
      },
      "source": [
        "# Iterating through the TF string array\n",
        "for string in tf_string_array:\n",
        "  print(string)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(b'TensorFlow', shape=(), dtype=string)\n",
            "tf.Tensor(b'Deep Learning', shape=(), dtype=string)\n",
            "tf.Tensor(b'AI', shape=(), dtype=string)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}