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Adding TimeStopping to tfa.callback (#757)
* added tutorial and code for TimeStopping
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‎docs/tutorials/time_stopping.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### Copyright 2019 The TensorFlow Authors."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"# you may not use this file except in compliance with the License.\n",
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"# You may obtain a copy of the License at\n",
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"#\n",
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"# https://www.apache.org/licenses/LICENSE-2.0\n",
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"#\n",
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"# Unless required by applicable law or agreed to in writing, software\n",
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"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"# See the License for the specific language governing permissions and\n",
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"# limitations under the License."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# TensorFlow Addons Callbacks: TimeStopping"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
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" <td>\n",
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" <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",
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" </td>\n",
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" <td>\n",
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" <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",
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" </td>\n",
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" <td>\n",
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" <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",
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" </td>\n",
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" <td>\n",
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" <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",
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" </td>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Overview\n",
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"This notebook will demonstrate how to use TimeStopping Callback in TensorFlow Addons."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install -q --no-deps tensorflow-addons~=0.6"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"try:\n",
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" # %tensorflow_version only exists in Colab.\n",
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" %tensorflow_version 2.x\n",
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"except Exception:\n",
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" pass"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"import tensorflow_addons as tfa\n",
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"\n",
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"import tensorflow.keras as keras\n",
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"from tensorflow.keras.datasets import mnist\n",
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"from tensorflow.keras.models import Sequential\n",
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"from tensorflow.keras.layers import Dense, Dropout, Flatten"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Import and Normalize Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"# the data, split between train and test sets\n",
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"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
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"# normalize data\n",
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"x_train, x_test = x_train / 255.0, x_test / 255.0"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Build Simple MNIST CNN Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"# build the model using the Sequential API\n",
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"model = Sequential()\n",
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"model.add(Flatten(input_shape=(28, 28)))\n",
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"model.add(Dense(128, activation='relu'))\n",
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"model.add(Dropout(0.2))\n",
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"model.add(Dense(10, activation='softmax'))\n",
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"\n",
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"model.compile(optimizer='adam',\n",
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" loss = 'sparse_categorical_crossentropy',\n",
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" metrics=['accuracy'])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Simple TimeStopping Usage"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Train on 60000 samples, validate on 10000 samples\n",
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"Epoch 1/100\n",
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"60000/60000 [==============================] - 2s 28us/sample - loss: 0.3357 - accuracy: 0.9033 - val_loss: 0.1606 - val_accuracy: 0.9533\n",
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"Epoch 2/100\n",
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"60000/60000 [==============================] - 1s 23us/sample - loss: 0.1606 - accuracy: 0.9525 - val_loss: 0.1104 - val_accuracy: 0.9669\n",
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"Epoch 3/100\n",
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"60000/60000 [==============================] - 1s 24us/sample - loss: 0.1185 - accuracy: 0.9645 - val_loss: 0.0949 - val_accuracy: 0.9704\n",
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"Epoch 4/100\n",
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"60000/60000 [==============================] - 1s 25us/sample - loss: 0.0954 - accuracy: 0.9713 - val_loss: 0.0854 - val_accuracy: 0.9740\n",
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"Timed stopping at epoch 4 after training for 0:00:05\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<tensorflow.python.keras.callbacks.History at 0x110af0ef0>"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# initialize TimeStopping callback \n",
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"time_stopping_callback = tfa.callbacks.TimeStopping(seconds=5, verbose=1)\n",
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"\n",
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"# train the model with tqdm_callback\n",
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"# make sure to set verbose = 0 to disable\n",
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"# the default progress bar.\n",
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"model.fit(x_train, y_train,\n",
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" batch_size=64,\n",
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" epochs=100,\n",
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" callbacks=[time_stopping_callback],\n",
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" validation_data=(x_test, y_test))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎tensorflow_addons/callbacks/BUILD

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name = "callbacks",
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srcs = [
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"__init__.py",
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"time_stopping.py",
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"tqdm_progress_bar.py",
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],
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deps = [

‎tensorflow_addons/callbacks/README.md

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## Maintainers
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| Submodule | Maintainers | Contact Info |
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|:---------- |:------------- |:--------------|
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| time_stopping | @shun-lin | [email protected] |
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| tqdm_progress_bar | @shun-lin | [email protected] |
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## Contents
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| Submodule | Callback | Reference |
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|:----------------------- |:-------------------|:---------------|
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| time_stopping | TimeStopping | N/A |
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| tqdm_progress_bar | TQDMProgressBar | https://tqdm.github.io/ |
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## Contribution Guidelines
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#### Standard API
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In order to conform with the current API standard, all callbacks

‎tensorflow_addons/callbacks/__init__.py

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from __future__ import division
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from __future__ import print_function
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from tensorflow_addons.callbacks.tqdm_progress_bar import TQDMProgressBar
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from tensorflow_addons.callbacks.time_stopping import TimeStopping
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from tensorflow_addons.callbacks.tqdm_progress_bar import TQDMProgressBar

‎tensorflow_addons/callbacks/time_stopping.py

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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Callback that stops training when a specified amount of time has passed."""
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from __future__ import absolute_import, division, print_function
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import datetime
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import time
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import tensorflow as tf
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from tensorflow.keras.callbacks import Callback
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@tf.keras.utils.register_keras_serializable(package='Addons')
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class TimeStopping(Callback):
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"""Stop training when a specified amount of time has passed.
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Args:
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seconds: maximum amount of time before stopping.
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Defaults to 86400 (1 day).
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verbose: verbosity mode. Defaults to 0.
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"""
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def __init__(self, seconds=86400, verbose=0):
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super(TimeStopping, self).__init__()
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self.seconds = seconds
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self.verbose = verbose
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def on_train_begin(self, logs=None):
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self.stopping_time = time.time() + self.seconds
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def on_epoch_end(self, epoch, logs={}):
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if time.time() >= self.stopping_time:
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self.model.stop_training = True
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self.stopped_epoch = epoch
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def on_train_end(self, logs=None):
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if self.verbose > 0:
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formatted_time = datetime.timedelta(seconds=self.seconds)
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msg = 'Timed stopping at epoch {} after training for {}'.format(
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self.stopped_epoch + 1, formatted_time)
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print(msg)
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def get_config(self):
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config = {
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'seconds': self.seconds,
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'verbose': self.verbose,
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}
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base_config = super(TimeStopping, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))

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