docs/tutorials/tqdm_progress_bar.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MyujzrAv2Vpk"
   },
   "source": [
    "##### Copyright 2020 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "id": "rTUqXTqa2Vpm"
   },
   "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": "rNnfCHh82Vpq"
   },
   "source": [
    "# TensorFlow Addons Callbacks: TQDM Progress Bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4qrDJoTw2Vps"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/addons/tutorials/tqdm_progress_bar\"><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/tqdm_progress_bar.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/tqdm_progress_bar.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/tqdm_progress_bar.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bVS_PkvX2Vpt"
   },
   "source": [
    "## Overview\n",
    "This notebook will demonstrate how to use TQDMCallback in TensorFlow Addons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sRldODz32Vpu"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "H0yZwcvcR4Gc"
   },
   "outputs": [],
   "source": [
    "!pip install -U tensorflow-addons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "etYr-Suo4KYj"
   },
   "outputs": [],
   "source": [
    "!pip install -q \"tqdm>=4.36.1\"\n",
    "\n",
    "import tensorflow as tf\n",
    "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": "code",
   "execution_count": null,
   "metadata": {
    "id": "SfXA0mI13pSE"
   },
   "outputs": [],
   "source": [
    "import tqdm\n",
    "\n",
    "# quietly deep-reload tqdm\n",
    "import sys\n",
    "from IPython.lib import deepreload\n",
    "\n",
    "stdout = sys.stdout\n",
    "sys.stdout = open(\"junk\", \"w\")\n",
    "deepreload.reload(tqdm)\n",
    "sys.stdout = stdout\n",
    "\n",
    "tqdm.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2RGuwIwe2Vp7"
   },
   "source": [
    "## Import and Normalize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qKfrsOSP2Vp8"
   },
   "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": "ORtL0s4X2VqB"
   },
   "source": [
    "## Build Simple MNIST CNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "z8uAGGV32VqC"
   },
   "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": "YWOnH1ga2VqF"
   },
   "source": [
    "## Default TQDMCallback Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Vl_oj_OW2VqG"
   },
   "outputs": [],
   "source": [
    "# initialize tqdm callback with default parameters\n",
    "tqdm_callback = tfa.callbacks.TQDMProgressBar()\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=10,\n",
    "    verbose=0,\n",
    "    callbacks=[tqdm_callback],\n",
    "    validation_data=(x_test, y_test),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uFvBfwJN2VqK"
   },
   "source": [
    "**Below is the expected output when you run the cell above**\n",
    "![TQDM Progress Bar Figure](https://raw.githubusercontent.com/tensorflow/addons/59961669a0e21eb4c045d4ad38d008a529d566c2/docs/tutorials/assets/tqdm_progress_bar_demo.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Np3dD8bhe10E"
   },
   "outputs": [],
   "source": [
    "# TQDMProgressBar() also works with evaluate()\n",
    "model.evaluate(x_test, y_test, batch_size=64, callbacks=[tqdm_callback], verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "36WRBMo7e10I"
   },
   "source": [
    "**Below is the expected output when you run the cell above**\n",
    "![TQDM Evaluate Progress Bar Figure](https://user-images.githubusercontent.com/20843596/79412655-a57cef00-7fc3-11ea-9bea-ade8ee8dad58.PNG)"
   ]
  }
 ],
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