{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Tensorflow_Eager_Exec-1.x.-2.0ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/raqueeb/TensorFlow2/blob/master/Tensorflow_Eager_Exec_1_x_2_0ipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "EM7xU9c7Qo8Z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# টেন্সর-ফ্লো ১.x ভার্সন \n",
        "# Tensorflow Eager mode নেই "
      ]
    },
    {
      "metadata": {
        "id": "917g8FH5Qo8a",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "F_KhZi5BTZpY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# এখন কি আছে?"
      ]
    },
    {
      "metadata": {
        "id": "Jg2No-evRzKe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5ffe72d3-cc60-4a32-d03a-3f281744e92a"
      },
      "cell_type": "code",
      "source": [
        "print(tf.__version__)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.13.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "DbKe0HiHQo8e",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Eager Enabled নেই (Default হিসেবে)"
      ]
    },
    {
      "metadata": {
        "id": "LaUN8hSzQo8f",
        "colab_type": "code",
        "outputId": "575c3274-b98a-494f-a726-fe2a79937033",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "tf.executing_eagerly()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "umwa9KWjSjwi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# tf.matmul: Multiply Two Matricies Using TensorFlow MatMul"
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "id": "ZdkL5GI6Qo8i",
        "colab_type": "code",
        "outputId": "98abf679-cd55-4854-d746-e6c2d57fe2ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "x = [[2., 2.],\n",
        "     [1., 0.]]\n",
        "\n",
        "m = tf.matmul(x, x)\n",
        "\n",
        "m"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'MatMul_1:0' shape=(2, 2) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "PpdhriR2T1bY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# টেন্সর-ফ্লো ২.০ ইনস্টলেশন \n",
        "\n",
        "কিছু জিনিস ঠিকমতো কাজ না করতে পারে, টেস্ট করতে থাকুন  "
      ]
    },
    {
      "metadata": {
        "id": "1H1iDGiDTpdr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "outputId": "1de8cd03-d374-4dba-9147-a6a672f65013"
      },
      "cell_type": "code",
      "source": [
        "!pip install --upgrade tensorflow-gpu==2.0.0-alpha0"
      ],
      "execution_count": 10,
      "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 62kB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: 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, skipping upgrade: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.7)\n",
            "Collecting 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",
            "\u001b[K     |████████████████████████████████| 419kB 40.7MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: 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, skipping upgrade: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.7.1)\n",
            "Requirement already satisfied, skipping upgrade: 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, skipping upgrade: 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, skipping upgrade: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (0.33.1)\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",
            "\u001b[K     |████████████████████████████████| 3.0MB 31.4MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: 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",
            "Requirement already satisfied, skipping upgrade: 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, skipping upgrade: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0-alpha0) (1.0.9)\n",
            "Collecting google-pasta>=0.1.2 (from tensorflow-gpu==2.0.0-alpha0)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/64/bb/f1bbc131d6294baa6085a222d29abadd012696b73dcbf8cf1bf56b9f082a/google_pasta-0.1.5-py3-none-any.whl (51kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 21.6MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: 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",
            "Requirement already satisfied, skipping upgrade: 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, skipping upgrade: 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, skipping upgrade: 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",
            "Installing collected packages: tf-estimator-nightly, tb-nightly, google-pasta, tensorflow-gpu\n",
            "Successfully installed google-pasta-0.1.5 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": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "sqqaXABD53ND",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "m1V3nWne55Aa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "42af5bc0-7f5d-47e6-bbc2-ecdd98c6aee2"
      },
      "cell_type": "code",
      "source": [
        "tf.executing_eagerly()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "nelIcfzlT9Hj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ace48694-d669-4f6a-d883-edd2741183bc"
      },
      "cell_type": "code",
      "source": [
        "tf.__version__"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.0.0-alpha0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "pmGNj0YNUGVz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "00ae5cc7-83bd-4e03-f527-3188fc33fad8"
      },
      "cell_type": "code",
      "source": [
        "x = [[2., 2.],\n",
        "     [1., 0.]]\n",
        "\n",
        "m = tf.matmul(x, x)\n",
        "\n",
        "m"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=2, shape=(2, 2), dtype=float32, numpy=\n",
              "array([[6., 4.],\n",
              "       [2., 2.]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "XNWuHyXwQo8l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9ee0696a-cfc2-4a21-f879-90ca04f2a78c"
      },
      "cell_type": "code",
      "source": [
        "m.numpy()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6., 4.],\n",
              "       [2., 2.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    }
  ]
}