TensorFlow Eager Execution

Eager_Execution_Disabled.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Eager_execution_d.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bzIFsLrNz3e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "tf.compat.v1.disable_eager_execution()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "joWTC7PzPIW_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "afe15c26-4bea-4964-e1b8-5b09af1446c3"
      },
      "source": [
        "tf.executing_eagerly()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [
            "py"
          ],
          "id": ""
        },
        "colab_type": "code",
        "outputId": "301f0cce-d7ff-4a55-db3a-e72dae8ff1fe",
        "id": "vSTmGyALNcUn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x = [[2.]]\n",
        "m = tf.matmul(x, x)\n",
        "print(\"hello, {}\".format(m))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello, Tensor(\"MatMul:0\", shape=(1, 1), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "33b34bce-624c-4eb2-f95b-6068461398c0",
        "id": "xk136sOrNcUq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "a = tf.constant([[1, 2],[3, 4]])\n",
        "print(a)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"Const:0\", shape=(2, 2), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "attributes": {
          "classes": [
            "py"
          ],
          "id": ""
        },
        "colab_type": "code",
        "id": "Ui025t1qqEfm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "e1f74f08-92fc-4882-95bd-52bd254e2302"
      },
      "source": [
        "# Use NumPy values\n",
        "import numpy as np\n",
        "\n",
        "c = np.multiply(a, a)\n",
        "print(c)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NotImplementedError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-60ab3ac37743>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\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      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\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_core/python/framework/ops.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    734\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__array__\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[1;32m    735\u001b[0m     raise NotImplementedError(\"Cannot convert a symbolic Tensor ({}) to a numpy\"\n\u001b[0;32m--> 736\u001b[0;31m                               \" array.\".format(self.name))\n\u001b[0m\u001b[1;32m    737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    738\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__len__\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;31mNotImplementedError\u001b[0m: Cannot convert a symbolic Tensor (Const:0) to a numpy array."
          ]
        }
      ]
    }
  ]
}

Eager_Execution_Enabled.ipynb