{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "MyujzrAv2Vpk"
},
"source": [
"##### Copyright 2020 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"cellView": "form",
"execution": {
"iopub.execute_input": "2022-06-16T06:38:24.022581Z",
"iopub.status.busy": "2022-06-16T06:38:24.022070Z",
"iopub.status.idle": "2022-06-16T06:38:24.025561Z",
"shell.execute_reply": "2022-06-16T06:38:24.025061Z"
},
"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": [
""
]
},
{
"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": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2022-06-16T06:38:24.029004Z",
"iopub.status.busy": "2022-06-16T06:38:24.028432Z",
"iopub.status.idle": "2022-06-16T06:38:25.800298Z",
"shell.execute_reply": "2022-06-16T06:38:25.799501Z"
},
"id": "H0yZwcvcR4Gc"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting tensorflow-addons\r\n",
" Using cached tensorflow_addons-0.17.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\r\n",
"Requirement already satisfied: packaging in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from tensorflow-addons) (21.3)\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting typeguard>=2.7\r\n",
" Using cached typeguard-2.13.3-py3-none-any.whl (17 kB)\r\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /tmpfs/src/tf_docs_env/lib/python3.9/site-packages (from packaging->tensorflow-addons) (3.0.9)\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Installing collected packages: typeguard, tensorflow-addons\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Successfully installed tensorflow-addons-0.17.1 typeguard-2.13.3\r\n"
]
}
],
"source": [
"!pip install -U tensorflow-addons"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2022-06-16T06:38:25.804281Z",
"iopub.status.busy": "2022-06-16T06:38:25.803739Z",
"iopub.status.idle": "2022-06-16T06:38:28.940935Z",
"shell.execute_reply": "2022-06-16T06:38:28.940189Z"
},
"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": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2022-06-16T06:38:28.945399Z",
"iopub.status.busy": "2022-06-16T06:38:28.944964Z",
"iopub.status.idle": "2022-06-16T06:38:28.985288Z",
"shell.execute_reply": "2022-06-16T06:38:28.984655Z"
},
"id": "SfXA0mI13pSE"
},
"outputs": [
{
"data": {
"text/plain": [
"'4.64.0'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"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": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2022-06-16T06:38:28.988551Z",
"iopub.status.busy": "2022-06-16T06:38:28.988037Z",
"iopub.status.idle": "2022-06-16T06:38:29.491337Z",
"shell.execute_reply": "2022-06-16T06:38:29.490625Z"
},
"id": "qKfrsOSP2Vp8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 8192/11490434 [..............................] - ETA: 0s"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"11490434/11490434 [==============================] - 0s 0us/step\n"
]
}
],
"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": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2022-06-16T06:38:29.495408Z",
"iopub.status.busy": "2022-06-16T06:38:29.494919Z",
"iopub.status.idle": "2022-06-16T06:38:32.451684Z",
"shell.execute_reply": "2022-06-16T06:38:32.451023Z"
},
"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(optimizer='adam',\n",
" loss = 'sparse_categorical_crossentropy',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YWOnH1ga2VqF"
},
"source": [
"## Default TQDMCallback Usage"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2022-06-16T06:38:32.456020Z",
"iopub.status.busy": "2022-06-16T06:38:32.455494Z",
"iopub.status.idle": "2022-06-16T06:38:52.837884Z",
"shell.execute_reply": "2022-06-16T06:38:52.837215Z"
},
"id": "Vl_oj_OW2VqG"
},
"outputs": [
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"model_id": "fdb7246adb4744a88cccea42926272b1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Training: 0%| 0/10 ETA: ?s, ?epochs/s"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n"
]
},
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"0/938 ETA: ?s - "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 2/10\n"
]
},
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"0/938 ETA: ?s - "
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},
"metadata": {},
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{
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"output_type": "stream",
"text": [
"Epoch 3/10\n"
]
},
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"0/938 ETA: ?s - "
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},
"metadata": {},
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"name": "stdout",
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"text": [
"Epoch 4/10\n"
]
},
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"0/938 ETA: ?s - "
]
},
"metadata": {},
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"output_type": "stream",
"text": [
"Epoch 5/10\n"
]
},
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"0/938 ETA: ?s - "
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"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 6/10\n"
]
},
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"0/938 ETA: ?s - "
]
},
"metadata": {},
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"output_type": "stream",
"text": [
"Epoch 7/10\n"
]
},
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"0/938 ETA: ?s - "
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"text": [
"Epoch 8/10\n"
]
},
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"0/938 ETA: ?s - "
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"text": [
"Epoch 9/10\n"
]
},
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"0/938 ETA: ?s - "
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"text": [
"Epoch 10/10\n"
]
},
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{
"data": {
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""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"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(x_train, y_train,\n",
" batch_size=64,\n",
" epochs=10,\n",
" verbose=0,\n",
" callbacks=[tqdm_callback],\n",
" validation_data=(x_test, y_test))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uFvBfwJN2VqK"
},
"source": [
"**Below is the expected output when you run the cell above**\n",
""
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2022-06-16T06:38:52.843228Z",
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"shell.execute_reply": "2022-06-16T06:38:53.189291Z"
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},
"outputs": [
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"text/plain": [
"0/157 ETA: ?s - Evaluating"
]
},
"metadata": {},
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},
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"data": {
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},
"execution_count": 8,
"metadata": {},
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}
],
"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",
""
]
}
],
"metadata": {
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"toc_visible": true
},
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![](\"https://www.tensorflow.org/images/tf_logo_32px.png\"){kind=logo}
View on TensorFlow.org\n",
" ![](\"https://www.tensorflow.org/images/colab_logo_32px.png\"){kind=logo}
Run in Google Colab\n",
" ![](\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\")
View source on GitHub\n",
" ![](\"https://www.tensorflow.org/images/download_logo_32px.png\"){kind=logo}
Download notebook\n",
"