control_dependencies.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "control_dependencies.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "tr1-hpjljUK5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "control_dependencies で直感と反するケース"
      ]
    },
    {
      "metadata": {
        "id": "gaS0Ok29lkq2",
        "colab_type": "code",
        "outputId": "4dbbf04c-a558-4f36-f3a8-25ff7dd98b62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.12.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "WWKepU5DUIcu",
        "colab_type": "code",
        "outputId": "83cfdc87-8144-4ffd-d1c6-96c42446e058",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "cell_type": "code",
      "source": [
        "tf.reset_default_graph()\n",
        "\n",
        "x = tf.random.normal((1,))\n",
        "y = tf.get_variable('y', shape=(1,), dtype=tf.float32)\n",
        "assign = y.assign(x)\n",
        "z = y + 1\n",
        "with tf.control_dependencies([assign]):\n",
        "  w = z + 1\n",
        "  \n",
        "sess = tf.InteractiveSession()\n",
        "sess.run(tf.initializers.global_variables())\n",
        "\n",
        "for _ in range(10):\n",
        "  print(sess.run([y, z, w]))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([0.21503043], dtype=float32), array([1.2150304], dtype=float32), array([2.2150304], dtype=float32)]\n",
            "[array([0.28964567], dtype=float32), array([1.2896457], dtype=float32), array([2.2896457], dtype=float32)]\n",
            "[array([0.00648436], dtype=float32), array([-0.7190591], dtype=float32), array([0.2809409], dtype=float32)]\n",
            "[array([0.49580196], dtype=float32), array([1.0064844], dtype=float32), array([2.0064845], dtype=float32)]\n",
            "[array([-1.0288614], dtype=float32), array([1.4958019], dtype=float32), array([2.495802], dtype=float32)]\n",
            "[array([-2.034961], dtype=float32), array([-0.0288614], dtype=float32), array([0.9711386], dtype=float32)]\n",
            "[array([-2.034961], dtype=float32), array([-1.034961], dtype=float32), array([-0.03496099], dtype=float32)]\n",
            "[array([0.02698239], dtype=float32), array([1.0269824], dtype=float32), array([2.0269823], dtype=float32)]\n",
            "[array([0.7145383], dtype=float32), array([1.7145383], dtype=float32), array([2.7145383], dtype=float32)]\n",
            "[array([2.2762003], dtype=float32), array([3.2762003], dtype=float32), array([4.2762003], dtype=float32)]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py:1702: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}