{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wJcYs_ERTnnI"
   },
   "source": [
    "##### Copyright 2021 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2022-12-14T20:23:13.978589Z",
     "iopub.status.busy": "2022-12-14T20:23:13.978068Z",
     "iopub.status.idle": "2022-12-14T20:23:13.981748Z",
     "shell.execute_reply": "2022-12-14T20:23:13.981219Z"
    },
    "id": "HMUDt0CiUJk9"
   },
   "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": "77z2OchJTk0l"
   },
   "source": [
    "# SessionRunHook을 Keras 콜백으로 마이그레이션하기\n",
    "\n",
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td><a target=\"_blank\" href=\"https://www.tensorflow.org/guide/migrate/sessionrunhook_callback\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">TensorFlow.org에서보기</a></td>\n",
    "  <td>     <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ko/guide/migrate/sessionrunhook_callback.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Google Colab에서 실행하기</a>\n",
    "</td>\n",
    "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ko/guide/migrate/sessionrunhook_callback.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">GitHub에서 소스 보기</a></td>\n",
    "  <td>     <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ko/guide/migrate/sessionrunhook_callback.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">노트북 다운로드하기</a>   </td>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KZHPY55aFyXT"
   },
   "source": [
    "TensorFlow 1에서 훈련 동작을 사용자 정의하려면 `tf.estimator.Estimator`와 함께 `tf.estimator.SessionRunHook`를 사용해야 합니다. 이 가이드는 `tf.keras.callbacks.Callback` API를 사용하여 `SessionRunHook`에서 TensorFlow 2의 사용자 정의 콜백으로 마이그레이션하는 방법을 설명합니다. 이 API는 훈련용 Keras `Model.fit`(`Model.evaluate` 및 `Model.predict`도 동일)와 함께 작동합니다. 훈련을 진행하는 동안 초당 예제를 측정하는 `SessionRunHook` 및 `Callback` 작업을 구현하여 이를 수행하는 방법을 배우게 됩니다.\n",
    "\n",
    "콜백의 예제로는 체크포인트 저장(`tf.keras.callbacks.ModelCheckpoint`) 및 [TensorBoard](%60tf.keras.callbacks.TensorBoard%60) 요약문 작성이 있습니다. Keras [콜백](https://www.tensorflow.org/guide/keras/custom_callback)은 내장된 Keras `Model.fit`/`Model.evaluate`/`Model.predict` API에서 훈련/평가/예측을 진행하는 동안 서로 다른 지점에서 호출되는 객체입니다. 콜백에 대한 자세한 내용은 `tf.keras.callbacks.Callback` API 문서와 [직접 콜백 작성하기](https://www.tensorflow.org/guide/keras/custom_callback.ipynb/) 및 [내장 메서드를 사용하여 훈련하고 평가하기](https://www.tensorflow.org/guide/keras/train_and_evaluate)(*콜백 사용하기* 섹션) 가이드를 참조하세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "29da56bf859d"
   },
   "source": [
    "## 설치하기\n",
    "\n",
    "데모를 위해 가져오기 및 간단한 데이터세트로 시작해 보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:23:13.985303Z",
     "iopub.status.busy": "2022-12-14T20:23:13.984774Z",
     "iopub.status.idle": "2022-12-14T20:23:15.896340Z",
     "shell.execute_reply": "2022-12-14T20:23:15.895598Z"
    },
    "id": "296d8b0DoKpV"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-14 20:23:14.924051: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
      "2022-12-14 20:23:14.924145: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
      "2022-12-14 20:23:14.924155: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.compat.v1 as tf1\n",
    "\n",
    "import time\n",
    "from datetime import datetime\n",
    "from absl import flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:23:15.900469Z",
     "iopub.status.busy": "2022-12-14T20:23:15.900064Z",
     "iopub.status.idle": "2022-12-14T20:23:15.904344Z",
     "shell.execute_reply": "2022-12-14T20:23:15.903722Z"
    },
    "id": "xVGYtUXyXNuE"
   },
   "outputs": [],
   "source": [
    "features = [[1., 1.5], [2., 2.5], [3., 3.5]]\n",
    "labels = [[0.3], [0.5], [0.7]]\n",
    "eval_features = [[4., 4.5], [5., 5.5], [6., 6.5]]\n",
    "eval_labels = [[0.8], [0.9], [1.]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ON4zQifT0Vec"
   },
   "source": [
    "## TensorFlow 1: tf.estimator API로 사용자 정의 SessionRunHook 생성하기\n",
    "\n",
    "다음 TensorFlow 1 예제는 훈련을 진행하는 동안 초당 예제를 측정하는 사용자 정의 `SessionRunHook`를 설정하는 방법을 보여줍니다. 후크(`LoggerHook`)를 생성한 후 `tf.estimator.Estimator.train`의 `hooks` 매개변수에 전달합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:23:15.907694Z",
     "iopub.status.busy": "2022-12-14T20:23:15.907312Z",
     "iopub.status.idle": "2022-12-14T20:23:15.911798Z",
     "shell.execute_reply": "2022-12-14T20:23:15.911232Z"
    },
    "id": "S-myEclbXUL7"
   },
   "outputs": [],
   "source": [
    "def _input_fn():\n",
    "  return tf1.data.Dataset.from_tensor_slices(\n",
    "      (features, labels)).batch(1).repeat(100)\n",
    "\n",
    "def _model_fn(features, labels, mode):\n",
    "  logits = tf1.layers.Dense(1)(features)\n",
    "  loss = tf1.losses.mean_squared_error(labels=labels, predictions=logits)\n",
    "  optimizer = tf1.train.AdagradOptimizer(0.05)\n",
    "  train_op = optimizer.minimize(loss, global_step=tf1.train.get_global_step())\n",
    "  return tf1.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:23:15.914958Z",
     "iopub.status.busy": "2022-12-14T20:23:15.914447Z",
     "iopub.status.idle": "2022-12-14T20:23:20.496861Z",
     "shell.execute_reply": "2022-12-14T20:23:20.496239Z"
    },
    "id": "Xd9sPTkO0ZTD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmpi0__a12q\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpi0__a12q', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
      "graph_options {\n",
      "  rewrite_options {\n",
      "    meta_optimizer_iterations: ONE\n",
      "  }\n",
      "}\n",
      ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/training_util.py:396: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/adagrad.py:138: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done calling model_fn.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Create CheckpointSaverHook.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Graph was finalized.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Running local_init_op.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpi0__a12q/model.ckpt.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 2022-12-14 20:23:19.989508 , Step #: 0 , Examples per second: 2.6514216127414776\n",
      "INFO:tensorflow:loss = 1.8147948, step = 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 2022-12-14 20:23:20.022854 , Step #: 10 , Examples per second: 299.86730725234503\n",
      "Time: 2022-12-14 20:23:20.029819 , Step #: 20 , Examples per second: 1435.7171219278428\n",
      "Time: 2022-12-14 20:23:20.036575 , Step #: 30 , Examples per second: 1479.8899160256863\n",
      "Time: 2022-12-14 20:23:20.043157 , Step #: 40 , Examples per second: 1519.345069912338\n",
      "Time: 2022-12-14 20:23:20.049921 , Step #: 50 , Examples per second: 1478.2730060268566\n",
      "Time: 2022-12-14 20:23:20.056636 , Step #: 60 , Examples per second: 1489.2958846713773\n",
      "Time: 2022-12-14 20:23:20.063276 , Step #: 70 , Examples per second: 1505.9256067786873\n",
      "Time: 2022-12-14 20:23:20.069780 , Step #: 80 , Examples per second: 1537.5578283661425\n",
      "Time: 2022-12-14 20:23:20.076458 , Step #: 90 , Examples per second: 1497.5378463296202\n",
      "INFO:tensorflow:global_step/sec: 1055.75\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 2022-12-14 20:23:20.084892 , Step #: 100 , Examples per second: 1185.6689752650177\n",
      "INFO:tensorflow:loss = 1.4855434e-05, step = 100 (0.095 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 2022-12-14 20:23:20.092704 , Step #: 110 , Examples per second: 1280.0390636921293\n",
      "Time: 2022-12-14 20:23:20.099669 , Step #: 120 , Examples per second: 1435.7662684421318\n",
      "Time: 2022-12-14 20:23:20.106356 , Step #: 130 , Examples per second: 1495.5621322873953\n",
      "Time: 2022-12-14 20:23:20.113093 , Step #: 140 , Examples per second: 1484.289050888244\n",
      "Time: 2022-12-14 20:23:20.119825 , Step #: 150 , Examples per second: 1485.4455305284034\n",
      "Time: 2022-12-14 20:23:20.126593 , Step #: 160 , Examples per second: 1477.491897985064\n",
      "Time: 2022-12-14 20:23:20.133065 , Step #: 170 , Examples per second: 1545.2617617802011\n",
      "Time: 2022-12-14 20:23:20.139650 , Step #: 180 , Examples per second: 1518.410020634978\n",
      "Time: 2022-12-14 20:23:20.146197 , Step #: 190 , Examples per second: 1527.6456876456878\n",
      "INFO:tensorflow:global_step/sec: 1437.75\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 2022-12-14 20:23:20.154350 , Step #: 200 , Examples per second: 1226.4405391970527\n",
      "INFO:tensorflow:loss = 0.00070948945, step = 200 (0.069 sec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 2022-12-14 20:23:20.161802 , Step #: 210 , Examples per second: 1341.9196314307653\n",
      "Time: 2022-12-14 20:23:20.168527 , Step #: 220 , Examples per second: 1487.0254555768277\n",
      "Time: 2022-12-14 20:23:20.175179 , Step #: 230 , Examples per second: 1503.388651923008\n",
      "Time: 2022-12-14 20:23:20.181607 , Step #: 240 , Examples per second: 1555.4622659002412\n",
      "Time: 2022-12-14 20:23:20.188128 , Step #: 250 , Examples per second: 1533.7345961165759\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 2022-12-14 20:23:20.195322 , Step #: 260 , Examples per second: 1389.9009179176194\n",
      "Time: 2022-12-14 20:23:20.201964 , Step #: 270 , Examples per second: 1505.709362435382\n",
      "Time: 2022-12-14 20:23:20.208528 , Step #: 280 , Examples per second: 1523.4287374691269\n",
      "Time: 2022-12-14 20:23:20.214937 , Step #: 290 , Examples per second: 1560.1487873828298\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 300...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Saving checkpoints for 300 into /tmpfs/tmp/tmpi0__a12q/model.ckpt.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 300...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Loss for final step: 0.0005846145.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow_estimator.python.estimator.estimator.Estimator at 0x7f5941222d30>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class LoggerHook(tf1.train.SessionRunHook):\n",
    "  \"\"\"Logs loss and runtime.\"\"\"\n",
    "\n",
    "  def begin(self):\n",
    "    self._step = -1\n",
    "    self._start_time = time.time()\n",
    "    self.log_frequency = 10\n",
    "\n",
    "  def before_run(self, run_context):\n",
    "    self._step += 1\n",
    "\n",
    "  def after_run(self, run_context, run_values):\n",
    "    if self._step % self.log_frequency == 0:\n",
    "      current_time = time.time()\n",
    "      duration = current_time - self._start_time\n",
    "      self._start_time = current_time\n",
    "      examples_per_sec = self.log_frequency / duration\n",
    "      print('Time:', datetime.now(), ', Step #:', self._step,\n",
    "            ', Examples per second:', examples_per_sec)\n",
    "\n",
    "estimator = tf1.estimator.Estimator(model_fn=_model_fn)\n",
    "\n",
    "# Begin training.\n",
    "estimator.train(_input_fn, hooks=[LoggerHook()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3uZCDMrM2CEg"
   },
   "source": [
    "## TensorFlow 2: Model.fit용 사용자 정의 Keras 콜백 생성하기\n",
    "\n",
    "TensorFlow 2에서 훈련/평가에 내장 Keras `Model.fit`(또는 `Model.evaluate`)를 사용하는 경우 사용자 정의`tf. keras.callbacks.Callback`을 구성한 후 이를 `Model.fit`(또는 `Model.evaluate`)의 `callbacks` 매개변수로 전달합니다(자세한 내용은 [직접 콜백 작성하기](../..guide/keras/custom_callback.ipynb) 가이드에서 확인).\n",
    "\n",
    "아래 예제에서는 다양한 메트릭을 기록하는 사용자 정의 `tf.keras.callbacks.Callback`을 작성합니다. 초당 예제를 측정하며 이전 `SessionRunHook`의 메트릭과 비교할 수 있어야 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-14T20:23:20.500521Z",
     "iopub.status.busy": "2022-12-14T20:23:20.499912Z",
     "iopub.status.idle": "2022-12-14T20:23:21.640382Z",
     "shell.execute_reply": "2022-12-14T20:23:21.639706Z"
    },
    "id": "UbMPoiB92KRG"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 2022-12-14 20:23:21.131505 , Step #: 0 , Examples per second: 20.585601724471335\n",
      "Time: 2022-12-14 20:23:21.150332 , Step #: 10 , Examples per second: 531.072450555851\n",
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     "name": "stdout",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "{'loss': [0.3040516972541809]}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class CustomCallback(tf.keras.callbacks.Callback):\n",
    "\n",
    "    def on_train_begin(self, logs = None):\n",
    "      self._step = -1\n",
    "      self._start_time = time.time()\n",
    "      self.log_frequency = 10\n",
    "\n",
    "    def on_train_batch_begin(self, batch, logs = None):\n",
    "      self._step += 1\n",
    "\n",
    "    def on_train_batch_end(self, batch, logs = None):\n",
    "      if self._step % self.log_frequency == 0:\n",
    "        current_time = time.time()\n",
    "        duration = current_time - self._start_time\n",
    "        self._start_time = current_time\n",
    "        examples_per_sec = self.log_frequency / duration\n",
    "        print('Time:', datetime.now(), ', Step #:', self._step,\n",
    "              ', Examples per second:', examples_per_sec)\n",
    "\n",
    "callback = CustomCallback()\n",
    "\n",
    "dataset = tf.data.Dataset.from_tensor_slices(\n",
    "    (features, labels)).batch(1).repeat(100)\n",
    "\n",
    "model = tf.keras.models.Sequential([tf.keras.layers.Dense(1)])\n",
    "optimizer = tf.keras.optimizers.Adagrad(learning_rate=0.05)\n",
    "\n",
    "model.compile(optimizer, \"mse\")\n",
    "\n",
    "# Begin training.\n",
    "result = model.fit(dataset, callbacks=[callback], verbose = 0)\n",
    "# Provide the results of training metrics.\n",
    "result.history"
   ]
  },
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   "metadata": {
    "id": "EFqFi21Ftskq"
   },
   "source": [
    "## 다음 단계\n",
    "\n",
    "콜백에 대한 자세한 내용:\n",
    "\n",
    "- API 문서: `tf.keras.callbacks.Callback`\n",
    "- 가이드: [직접 콜백 작성하기](../..guide/keras/custom_callback.ipynb/)\n",
    "- 가이드: [내장 메서드를 사용하여 훈련하고 평가하기](https://www.tensorflow.org/guide/keras/train_and_evaluate)(*콜백 사용하기* 섹션)\n",
    "\n",
    "다음과 같은 마이그레이션 관련 리소스도 유용할 수 있습니다.\n",
    "\n",
    "- [조기 중단 마이그레이션 가이드](early_stopping.ipynb): 내장 조기 중단 콜백인 `tf.keras.callbacks.EarlyStopping`\n",
    "- [TensorBoard 마이그레이션 가이드](tensorboard.ipynb): TensorBoard를 사용하여 메트릭 추적 및 표시하기\n",
    "- [LoggingTensorHook 및 StopAtStepHook에서 Keras 콜백으로의 마이그레이션 가이드](logging_stop_hook.ipynb)"
   ]
  }
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