Colab_2_TensorFlow_2_0_Introduction.ipynb

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  "metadata": {
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/talachutla/camel/blob/master/Colab_2_TensorFlow_2_0_Introduction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "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",
        "outputId": "5ffd87b7-f6bc-4187-80f8-f428188f42a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "source": [
        "!pip install tensorflow-gpu==1.13.1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu==1.13.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7b/b1/0ad4ae02e17ddd62109cd54c291e311c4b5fd09b4d0678d3d6ce4159b0f0/tensorflow_gpu-1.13.1-cp36-cp36m-manylinux1_x86_64.whl (345.2MB)\n",
            "\u001b[K     |████████████████████████████████| 345.2MB 49kB/s \n",
            "\u001b[?25hRequirement already satisfied: tensorflow-estimator<1.14.0rc0,>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.13.0)\n",
            "Requirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (0.7.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.0.9)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.1.0)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (0.2.2)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (0.33.1)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.0.7)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.12.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.16.3)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.15.0)\n",
            "Requirement already satisfied: tensorboard<1.14.0,>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (1.13.1)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (0.7.1)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.13.1) (3.7.1)\n",
            "Requirement already satisfied: mock>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow-gpu==1.13.1) (2.0.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==1.13.1) (2.8.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow-gpu==1.13.1) (0.15.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow-gpu==1.13.1) (3.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==1.13.1) (41.0.1)\n",
            "Requirement already satisfied: pbr>=0.11 in /usr/local/lib/python3.6/dist-packages (from mock>=2.0.0->tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow-gpu==1.13.1) (5.2.0)\n",
            "Installing collected packages: tensorflow-gpu\n",
            "Successfully installed tensorflow-gpu-1.13.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQcq7zJ4cgf_"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKzynmqddNBO",
        "outputId": "490bd700-d4fe-4f3e-f16d-553bfa7027ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.13.1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6YBDabufXO1",
        "outputId": "5b26d206-51af-430a-ab18-94db406edf36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tensor"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'Const:0' shape=(2, 2) dtype=int32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdNftutxfZ5N",
        "outputId": "d4cd4c86-2159-4d80-9033-ef783a8822fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "source": [
        "# If a session is not initialized, we cannot access the values of the constant\n",
        "tensor.eval()"
      ],
      "execution_count": null,
      "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-28-16de3a9398ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\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/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, feed_dict, session)\u001b[0m\n\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m     \"\"\"\n\u001b[0;32m--> 695\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    696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    697\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/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   5165\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   5166\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-> 5167\u001b[0;31m       raise ValueError(\"Cannot evaluate tensor using `eval()`: No default \"\n\u001b[0m\u001b[1;32m   5168\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   5169\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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iM9jQMJhgGzi"
      },
      "source": [
        "session.run(tf.global_variables_initializer())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xk-Ng1_0fmeV"
      },
      "source": [
        "tensor_value = session.run(tensor)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4c_EfA0zgBb9",
        "outputId": "7a4c449b-4054-4eca-c319-428e3d6415f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "tensor_value"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[23,  4],\n",
              "       [32, 51]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMco0fladtkt",
        "outputId": "93a2f998-37f3-49d1-ae58-33e92bb25c0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "variable"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'Variable:0' shape=(2, 2) dtype=int32_ref>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OAZRkgwed9X-",
        "outputId": "c39ec80b-5a88-411b-b6fd-a81e42a9a79c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "source": [
        "# If a session is not initialized, we cannot access the values of the variable\n",
        "variable.eval()"
      ],
      "execution_count": null,
      "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-15-37927263c520>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\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/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variables.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, session)\u001b[0m\n\u001b[1;32m   1693\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   1694\u001b[0m     \"\"\"\n\u001b[0;32m-> 1695\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   1696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1697\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0minitialized_value\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/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, feed_dict, session)\u001b[0m\n\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m     \"\"\"\n\u001b[0;32m--> 695\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    696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    697\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/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   5165\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   5166\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-> 5167\u001b[0;31m       raise ValueError(\"Cannot evaluate tensor using `eval()`: No default \"\n\u001b[0m\u001b[1;32m   5168\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   5169\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": null,
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yMmNCDviex--",
        "outputId": "3a5f21aa-28c5-4f30-a1c7-fb15e1991e79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Running the eval method on the environment with the initialized session to retrieve values from it\n",
        "variable.eval(session)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[30, 20],\n",
              "       [10, 45]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "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": "XExgAvb-gkb9",
        "outputId": "d10d1948-0dd1-4584-8884-7ca52ff1efba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        }
      },
      "source": [
        "!pip install tensorflow-gpu==2.0.0-alpha0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu==2.0.0-alpha0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1a/66/32cffad095253219d53f6b6c2a436637bbe45ac4e7be0244557210dc3918/tensorflow_gpu-2.0.0a0-cp36-cp36m-manylinux1_x86_64.whl (332.1MB)\n",
            "\u001b[K     |████████████████████████████████| 332.1MB 57kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.16.3)\n",
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            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.12.0)\n",
            "Collecting tb-nightly<1.14.0a20190302,>=1.14.0a20190301 (from tensorflow-gpu==2.0.0-alpha0)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a9/51/aa1d756644bf4624c03844115e4ac4058eff77acd786b26315f051a4b195/tb_nightly-1.14.0a20190301-py3-none-any.whl (3.0MB)\n",
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            "\u001b[?25hRequirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.15.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.33.1)\n",
            "Collecting google-pasta>=0.1.2 (from tensorflow-gpu==2.0.0-alpha0)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f9/68/a14620bfb042691f532dcde8576ff82ee82e4c003cdc0a3dbee5f289cee6/google_pasta-0.1.6-py3-none-any.whl (51kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 24.0MB/s \n",
            "\u001b[?25hCollecting tf-estimator-nightly<1.14.0.dev2019030116,>=1.14.0.dev2019030115 (from tensorflow-gpu==2.0.0-alpha0)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/13/82/f16063b4eed210dc2ab057930ac1da4fbe1e91b7b051a6c8370b401e6ae7/tf_estimator_nightly-1.14.0.dev2019030115-py2.py3-none-any.whl (411kB)\n",
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            "\u001b[?25hRequirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (3.7.1)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.2.2)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.1.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.7)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.7.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha0) (0.15.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0-alpha0) (3.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==2.0.0-alpha0) (41.0.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0-alpha0) (2.8.0)\n",
            "Installing collected packages: tb-nightly, google-pasta, tf-estimator-nightly, tensorflow-gpu\n",
            "Successfully installed google-pasta-0.1.6 tb-nightly-1.14.0a20190301 tensorflow-gpu-2.0.0a0 tf-estimator-nightly-1.14.0.dev2019030115\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "tensorflow",
                  "tensorflow_estimator"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsQVt3TghDWN"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjZ7TmxNh3FE",
        "outputId": "4b0803f1-e4b1-4291-a863-545b2a2b1ce7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.0.0-alpha0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zu1SK9N-iLj-",
        "outputId": "37bef0cb-59bb-4015-f665-7fc88cc47084",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "tensor_20"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=0, 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",
        "outputId": "1e17e0ee-3477-4ce9-e35c-34a9218e1e5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Getting the shape of a tensor\n",
        "tensor_20.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KfR8I7EOituB"
      },
      "source": [
        "#### Getting the values of a constant"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C1mt_C-7iV3l",
        "outputId": "7fd6c3aa-e14e-4f14-dce6-bdb89d21beed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Getting the values straight from a TensorFlow constant with numpy, and without the need of a session\n",
        "tensor_20.numpy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[23,  4],\n",
              "       [32, 51]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uLZ7XAYjOO2"
      },
      "source": [
        "tensor_from_numpy = tf.constant(numpy_tensor)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltsz-6FrjWDE",
        "outputId": "213dff42-7a3a-42df-9db5-9678f7497c70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "tensor_from_numpy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=7, shape=(2, 2), dtype=int64, numpy=\n",
              "array([[23,  4],\n",
              "       [32, 51]])>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "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",
        "outputId": "8276a637-4633-4433-caaf-097b0784d863",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "tf2_variable = tf.Variable([[1., 2., 3.], [4., 5., 6.]])\n",
        "tf2_variable"
      ],
      "execution_count": null,
      "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": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oqi-QOsMWo7F"
      },
      "source": [
        "#### Getting the values of a variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCQr70r1IhLF",
        "outputId": "acaa9843-0462-4831-aa22-8850034a30b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "tf2_variable.numpy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 2., 3.],\n",
              "       [4., 5., 6.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ka8Tpnk_WvET"
      },
      "source": [
        "#### Changing a specific value in the variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23OsvfbXWtel",
        "outputId": "f171b421-8187-4fb1-ec9b-f94ee38cb44e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "tf2_variable[0, 2].assign(100)"
      ],
      "execution_count": null,
      "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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cSj0YvJkXZu8",
        "outputId": "56f67b34-9320-48d6-8808-35651b2c8ce0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "tf2_variable"
      ],
      "execution_count": null,
      "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": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QdbG1kvXD2O-"
      },
      "source": [
        "### Operations with tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8TE6VvuFDXd",
        "outputId": "5e87685b-48a6-4a2d-d268-a9b4be2cb769",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "tensor = tf.constant([[1, 2], [3, 4]])\n",
        "tensor"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=19, shape=(2, 2), dtype=int32, numpy=\n",
              "array([[1, 2],\n",
              "       [3, 4]], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9AelsMJIFYku"
      },
      "source": [
        "#### Addition between a scalar and a tensor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBHn9DTVjW6s",
        "outputId": "2e9cf13a-0482-4a05-feba-fc8c3ba20848",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "tensor + 2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=22, shape=(2, 2), dtype=int32, numpy=\n",
              "array([[3, 4],\n",
              "       [5, 6]], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQY7rduwFfEC"
      },
      "source": [
        "#### Multiplication between a scalar and a tensor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZkzLDnVFBZF",
        "outputId": "12bffe47-4147-476b-924c-aa15cd262354",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "tensor * 5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=25, shape=(2, 2), dtype=int32, numpy=\n",
              "array([[ 5, 10],\n",
              "       [15, 20]], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0algGv7HFrUI"
      },
      "source": [
        "#### Using Numpy functions on TensorFlow tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTeiyc6HFyIs",
        "outputId": "dff5e640-2e79-4dec-c9ca-3b8aac9357a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Getting the squares of all numbers in a TensorFlow tensor object\n",
        "np.square(tensor)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  4],\n",
              "       [ 9, 16]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zcR_Z_4vF2wG",
        "outputId": "fc9e42a0-d5e9-436b-8fbe-e24f7bda4c62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Getting the square root of all numbers in a tensorflow tensor object\n",
        "np.sqrt(tensor)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 1.41421356],\n",
              "       [1.73205081, 2.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UL--f1anIMRl"
      },
      "source": [
        "#### Dot product between two tensors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6DGgNB--F6Q1",
        "outputId": "1bcd95b9-2863-45b5-9764-84fb778e6501",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        " np.dot(tensor, tensor_20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 87, 106],\n",
              "       [197, 216]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12dtWEKjXl7l"
      },
      "source": [
        "### Strings in TensorFlow 2.0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fXj_Be2bXgdj",
        "outputId": "8babd4f5-a504-4950-91b7-8a0dfdbc61f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tf_string = tf.constant(\"TensorFlow\")\n",
        "tf_string"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=62, shape=(), dtype=string, numpy=b'TensorFlow'>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9VXL59XYPDG"
      },
      "source": [
        "#### Simple string operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sIYqcC9YjoS",
        "outputId": "01556bf0-d72c-4fac-d413-190ee80d8ea3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tf.strings.length(tf_string)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=49, shape=(), dtype=int32, numpy=10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxQRhILBYmiH",
        "outputId": "b5def63f-b3ae-4f65-b9bb-76ae4c867d7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tf.strings.unicode_decode(tf_string, \"UTF8\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=58, shape=(10,), dtype=int32, numpy=array([ 84, 101, 110, 115, 111, 114,  70, 108, 111, 119], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyFS4E9uZh21",
        "outputId": "5957ba70-c78f-4c9b-8f02-40ebea6a412c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "# Iterating through the TF string array\n",
        "for string in tf_string_array:\n",
        "  print(string)"
      ],
      "execution_count": null,
      "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"
        }
      ]
    }
  ]
}