{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TsHV-7cpVkyK"
      },
      "outputs": [],
      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "atWM-s8yVnfX",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TB0wBWfcVqHz"
      },
      "source": [
        "# 在笔记本中使用 TensorBoard\n",
        "\n",
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td><a target=\"_blank\" href=\"https://tensorflow.google.cn/tensorboard/tensorboard_in_notebooks\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 TensorFlow.org 上查看 </a></td>\n",
        "  <td>     <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tensorboard/tensorboard_in_notebooks.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 中运行</a> </td>\n",
        "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tensorboard/tensorboard_in_notebooks.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 GitHub 中查看源代码</a></td>\n",
        "  <td>     <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/tensorboard/tensorboard_in_notebooks.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载笔记本</a>   </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elH58gbhWAmn"
      },
      "source": [
        "TensorBoard 可以直接在诸如 [Colab](https://colab.research.google.com/) 和 [Jupyter](https://jupyter.org/) 一类的笔记本体验中使用。这有助于共享结果、将 TensorBoard 集成到现有工作流，以及在不进行任何本地安装的情况下使用 TensorBoard。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VszJNloY3ZU3"
      },
      "source": [
        "## 设置"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E6QhA_dp3eRq"
      },
      "source": [
        "首先，安装 TF 2.0 并加载 TensorBoard 笔记本扩展程序：\n",
        "\n",
        "**对于 Jupyter 用户**：如果您已经将 Jupyter 和 TensorBoard 安装在同一 virtualenv 中，那么您无需进行其他设置。如果您使用更复杂的设置，例如为不同 Conda/virtualenv 环境使用全局 Jupyter 安装和内核，则必须确保 `tensorboard` 二进制文件位于 Jupyter 笔记本上下文内的 `PATH` 中。执行此操作的一种方法是修改 `kernel_spec`，在 `PATH` 前添加环境的 `bin` 目录，[如此处所述](https://github.com/ipython/ipykernel/issues/395#issuecomment-479787997)。\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9w7Baxc8aCtJ"
      },
      "source": [
        "**对于 Docker 用户**：如果您[使用 TensorFlow 的 Nightly 版本运行 Jupyter Notebook 服务器的](https://docs.docker.com/install/) [Docker](https://tensorflow.google.cn/install/docker#examples_using_cpu-only_images) 镜像，则不仅要公开笔记本的端口，还要公开 TensorBoard 的端口。因此，使用以下命令运行容器：\n",
        "\n",
        "```\n",
        "docker run -it -p 8888:8888 -p 6006:6006 \\\n",
        "tensorflow/tensorflow:nightly-py3-jupyter\n",
        "```\n",
        "\n",
        "其中，`-p 6006` 为 TensorBoard 的默认端口。这将为您分配一个端口来运行一个 TensorBoard 实例。要运行并发实例，必须分配多个端口。此外，将 `--bind_all` 传递给 `%tensorboard` 可以在容器外公开端口。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "8p3Tbx8cWEFA",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "# Load the TensorBoard notebook extension\n",
        "%load_ext tensorboard"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GtR_cTTkf9G"
      },
      "source": [
        "导入 TensorFlow、日期时间和操作系统："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mVtYvbbIWRkV",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import datetime, os"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cu1fbH-S3oAX"
      },
      "source": [
        "## 在笔记本中使用 TensorBoard"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XfCa27_8kov6"
      },
      "source": [
        "下载 [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist) 数据集并对其进行缩放："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "z8b82G7YksOS",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "(x_train, y_train),(x_test, y_test) = fashion_mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lBk1BqAZKEKd"
      },
      "source": [
        "创建一个非常简单的模型："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OS7qGYiMKGQl",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "def create_model():\n",
        "  return tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28), name='layers_flatten'),\n",
        "    tf.keras.layers.Dense(512, activation='relu', name='layers_dense'),\n",
        "    tf.keras.layers.Dropout(0.2, name='layers_dropout'),\n",
        "    tf.keras.layers.Dense(10, activation='softmax', name='layers_dense_2')\n",
        "  ])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNaPPs5ZKNOV"
      },
      "source": [
        "使用 Keras 和 TensorBoard 回调训练模型："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "lpUO9HqUKP6z",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 11s 182us/sample - loss: 0.4976 - accuracy: 0.8204 - val_loss: 0.4143 - val_accuracy: 0.8538\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 10s 174us/sample - loss: 0.3845 - accuracy: 0.8588 - val_loss: 0.3855 - val_accuracy: 0.8626\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 10s 175us/sample - loss: 0.3513 - accuracy: 0.8705 - val_loss: 0.3740 - val_accuracy: 0.8607\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 11s 177us/sample - loss: 0.3287 - accuracy: 0.8793 - val_loss: 0.3596 - val_accuracy: 0.8719\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 11s 178us/sample - loss: 0.3153 - accuracy: 0.8825 - val_loss: 0.3360 - val_accuracy: 0.8782\n"
          ]
        }
      ],
      "source": [
        "def train_model():\n",
        "  \n",
        "  model = create_model()\n",
        "  model.compile(optimizer='adam',\n",
        "                loss='sparse_categorical_crossentropy',\n",
        "                metrics=['accuracy'])\n",
        "\n",
        "  logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
        "  tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
        "\n",
        "  model.fit(x=x_train, \n",
        "            y=y_train, \n",
        "            epochs=5, \n",
        "            validation_data=(x_test, y_test), \n",
        "            callbacks=[tensorboard_callback])\n",
        "\n",
        "train_model()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SxvXc4hoKW7d"
      },
      "source": [
        "使用[魔术命令](https://ipython.readthedocs.io/en/stable/interactive/magics.html)在笔记本中启动 TensorBoard："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KBHp6M_zgjp4",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "%tensorboard --logdir logs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Po7rTfQswAMT"
      },
      "source": [
        "<!-- <img class=\"tfo-display-only-on-site\" src=\"https://github.com/tensorflow/tensorboard/blob/master/docs/images/notebook_tensorboard.png?raw=1\"/> -->"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQq3UHgmLBpC"
      },
      "source": [
        "您现在可以查看 **Time Series**、**Graphs**、**Distributions** 等信息中心。某些信息中心在 Colab 中尚不可用（例如配置文件插件）。\n",
        "\n",
        "`%tensorboard` 魔术命令与 TensorBoard 命令行调用的格式基本相同，区别在于其开头带有 `%` 符号。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NiIMwOG8MR_g"
      },
      "source": [
        "您也可以在训练前启动 TensorBoard，对其进行监视："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qyI5lrXoMw9K",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "%tensorboard --logdir logs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ALxC8BbWWV91"
      },
      "source": [
        "<!-- <img class=\"tfo-display-only-on-site\" src=\"https://github.com/tensorflow/tensorboard/blob/master/docs/images/notebook_tensorboard_two_runs.png?raw=1\"/> -->"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUSM8yLrO2yZ"
      },
      "source": [
        "通过发出相同的命令，可以重用相同的 TensorBoard 后端。如果选择了其他日志目录，将打开新的 TensorBoard 实例。将自动管理端口。\n",
        "\n",
        "开始训练新模型，观察 TensorBoard 每 30 秒自动更新一次，或者使用右上角的按钮进行刷新："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "ixZlmtWhMyr4",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 11s 184us/sample - loss: 0.4968 - accuracy: 0.8223 - val_loss: 0.4216 - val_accuracy: 0.8481\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 11s 176us/sample - loss: 0.3847 - accuracy: 0.8587 - val_loss: 0.4056 - val_accuracy: 0.8545\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 11s 176us/sample - loss: 0.3495 - accuracy: 0.8727 - val_loss: 0.3600 - val_accuracy: 0.8700\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 11s 179us/sample - loss: 0.3282 - accuracy: 0.8795 - val_loss: 0.3636 - val_accuracy: 0.8694\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 11s 176us/sample - loss: 0.3115 - accuracy: 0.8839 - val_loss: 0.3438 - val_accuracy: 0.8764\n"
          ]
        }
      ],
      "source": [
        "train_model()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IlDz2oXBgnZ9"
      },
      "source": [
        "您可以使用 `tensorboard.notebook` API 进行更多控制："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ko9qeSQHLrEh",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Known TensorBoard instances:\n",
            "  - port 6006: logdir logs (started 0:00:54 ago; pid 265)\n"
          ]
        }
      ],
      "source": [
        "from tensorboard import notebook\n",
        "notebook.list() # View open TensorBoard instances"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hzm9DNVILxJe",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "# Control TensorBoard display. If no port is provided, \n",
        "# the most recently launched TensorBoard is used\n",
        "notebook.display(port=6006, height=1000) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "za2GqzKiWY-R"
      },
      "source": [
        "<!-- <img class=\"tfo-display-only-on-site\" src=\"https://github.com/tensorflow/tensorboard/blob/master/docs/images/notebook_tensorboard_tall.png?raw=1\"/> -->"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "tensorboard_in_notebooks.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}