docs/tutorials/time_stopping.ipynb

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 "cells": [
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   "metadata": {
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   "source": [
    "##### Copyright 2020 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
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   "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": "gyGdPCvQYjaI"
   },
   "source": [
    "# TensorFlow Addons Callbacks: TimeStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z5csJXPVYjaM"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/addons/tutorials/time_stopping\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/addons/blob/master/docs/tutorials/time_stopping.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/addons/blob/master/docs/tutorials/time_stopping.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "      <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/addons/docs/tutorials/time_stopping.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BJhody3KYjaP"
   },
   "source": [
    "## Overview\n",
    "This notebook will demonstrate how to use TimeStopping Callback in TensorFlow Addons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SaZsCaGbYjaU"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VgJGPL3ts_1i"
   },
   "outputs": [],
   "source": [
    "!pip install -U tensorflow-addons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fm_dHPvEYjar"
   },
   "outputs": [],
   "source": [
    "import tensorflow_addons as tfa\n",
    "\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vg0y1DrQYja4"
   },
   "source": [
    "## Import and Normalize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HydkzZTuYja8"
   },
   "outputs": [],
   "source": [
    "# the data, split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "# normalize data\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uX02I1kxYjbL"
   },
   "source": [
    "## Build Simple MNIST CNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Tlk0MyEfYjbN"
   },
   "outputs": [],
   "source": [
    "# build the model using the Sequential API\n",
    "model = Sequential()\n",
    "model.add(Flatten(input_shape=(28, 28)))\n",
    "model.add(Dense(128, activation=\"relu\"))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(10, activation=\"softmax\"))\n",
    "\n",
    "model.compile(\n",
    "    optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b5Xcyt0qYjbX"
   },
   "source": [
    "## Simple TimeStopping Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W82_IZ6iYjbZ"
   },
   "outputs": [],
   "source": [
    "# initialize TimeStopping callback\n",
    "time_stopping_callback = tfa.callbacks.TimeStopping(seconds=5, verbose=1)\n",
    "\n",
    "# train the model with tqdm_callback\n",
    "# make sure to set verbose = 0 to disable\n",
    "# the default progress bar.\n",
    "model.fit(\n",
    "    x_train,\n",
    "    y_train,\n",
    "    batch_size=64,\n",
    "    epochs=100,\n",
    "    callbacks=[time_stopping_callback],\n",
    "    validation_data=(x_test, y_test),\n",
    ")"
   ]
  }
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