{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "wJcYs_ERTnnI" }, "source": [ "##### Copyright 2021 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "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": [ "# Migrate SessionRunHook to Keras callbacks\n", "\n", "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", " View on TensorFlow.org\n", " \n", " \n", " \n", " Run in Google Colab\n", " \n", " \n", " \n", " View source on GitHub\n", " \n", " Download notebook\n", "
\n" ] }, { "cell_type": "markdown", "metadata": { "id": "KZHPY55aFyXT" }, "source": [ "In TensorFlow 1, to customize the behavior of training, you use `tf.estimator.SessionRunHook` with `tf.estimator.Estimator`. This guide demonstrates how to migrate from `SessionRunHook` to TensorFlow 2's custom callbacks with the `tf.keras.callbacks.Callback` API, which works with Keras `Model.fit` for training (as well as `Model.evaluate` and `Model.predict`). You will learn how to do this by implementing a `SessionRunHook` and a `Callback` task that measures examples per second during training.\n", "\n", "Examples of callbacks are checkpoint saving (`tf.keras.callbacks.ModelCheckpoint`) and [TensorBoard](`tf.keras.callbacks.TensorBoard`) summary writing. Keras [callbacks](https://www.tensorflow.org/guide/keras/custom_callback) are objects that are called at different points during training/evaluation/prediction in the built-in Keras `Model.fit`/`Model.evaluate`/`Model.predict` APIs. You can learn more about callbacks in the `tf.keras.callbacks.Callback` API docs, as well as the [Writing your own callbacks](https://www.tensorflow.org/guide/keras/custom_callback.ipynb/) and [Training and evaluation with the built-in methods](https://www.tensorflow.org/guide/keras/train_and_evaluate) (the *Using callbacks* section) guides." ] }, { "cell_type": "markdown", "metadata": { "id": "29da56bf859d" }, "source": [ "## Setup\n", "\n", "Start with imports and a simple dataset for demonstration purposes:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "296d8b0DoKpV" }, "outputs": [], "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": null, "metadata": { "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: Create a custom SessionRunHook with tf.estimator APIs\n", "\n", "The following TensorFlow 1 examples show how to set up a custom `SessionRunHook` that measures examples per second during training. After creating the hook (`LoggerHook`), pass it to the `hooks` parameter of `tf.estimator.Estimator.train`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "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": null, "metadata": { "id": "Xd9sPTkO0ZTD" }, "outputs": [], "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: Create a custom Keras callback for Model.fit\n", "\n", "In TensorFlow 2, when you use the built-in Keras `Model.fit` (or `Model.evaluate`) for training/evaluation, you can configure a custom `tf.keras.callbacks.Callback`, which you then pass to the `callbacks` parameter of `Model.fit` (or `Model.evaluate`). (Learn more in the [Writing your own callbacks](../..guide/keras/custom_callback.ipynb) guide.)\n", "\n", "In the example below, you will write a custom `tf.keras.callbacks.Callback` that logs various metrics—it will measure examples per second, which should be comparable to the metrics in the previous `SessionRunHook` example." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UbMPoiB92KRG" }, "outputs": [], "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" ] }, { "cell_type": "markdown", "metadata": { "id": "EFqFi21Ftskq" }, "source": [ "## Next steps\n", "\n", "Learn more about callbacks in:\n", "\n", "- API docs: `tf.keras.callbacks.Callback`\n", "- Guide: [Writing your own callbacks](../..guide/keras/custom_callback.ipynb/)\n", "- Guide: [Training and evaluation with the built-in methods](https://www.tensorflow.org/guide/keras/train_and_evaluate) (the *Using callbacks* section)\n", "\n", "You may also find the following migration-related resources useful:\n", "\n", "- The [Early stopping migration guide](early_stopping.ipynb): `tf.keras.callbacks.EarlyStopping` is a built-in early stopping callback\n", "- The [TensorBoard migration guide](tensorboard.ipynb): TensorBoard enables tracking and displaying metrics\n", "- The [LoggingTensorHook and StopAtStepHook to Keras callbacks migration guide](logging_stop_hook.ipynb)" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "sessionrunhook_callback.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }