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Add TensorBoard.dev getting started notebook (tensorflow#2865)
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‎docs/tbdev_getting_started.ipynb

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Getting Started with TensorBoard.dev",
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"provenance": [],
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"collapsed_sections": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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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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"id": "h3Nuf-G4xJ0u",
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"colab_type": "text"
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},
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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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"metadata": {
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"id": "zZ81_4tLxSvd",
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"colab_type": "code",
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"colab": {},
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"cellView": "form"
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},
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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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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "wNBP_f0QUTfO",
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"colab_type": "text"
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},
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"source": [
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"# Getting started with [TensorBoard.dev](https://tensorboard.dev)"
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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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"id": "DLXZ3t1PWdOp",
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"colab_type": "text"
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},
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"source": [
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"[TensorBoard.dev](https://tensorboard.dev) provides a managed [TensorBoard](https://tensorflow.org/tensorboard) experience that lets you upload and share your ML experiment results with everyone.\n",
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"\n",
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"This notebook trains a simple model and shows how to upload the logs to TensorBoard.dev."
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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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"id": "yjBn-ptXTppA",
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"colab_type": "text"
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},
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"source": [
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"### Setup and imports"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "5C8BOea_rF49",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"%tensorflow_version 2.x\n",
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"!pip install -U tensorboard >piplog 2>&1"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "L3ns52Luracm",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"import tensorflow as tf\n",
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"import datetime"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "GqUABmUTT1Cl",
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"colab_type": "text"
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},
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"source": [
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"### Train a simple model and create TensorBoard logs"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "LZExSr2Qrc5S",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"mnist = tf.keras.datasets.mnist\n",
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"\n",
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"(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
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"x_train, x_test = x_train / 255.0, x_test / 255.0\n",
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"\n",
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"def create_model():\n",
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" return tf.keras.models.Sequential([\n",
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" tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
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" tf.keras.layers.Dense(512, activation='relu'),\n",
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" tf.keras.layers.Dropout(0.2),\n",
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" tf.keras.layers.Dense(10, activation='softmax')\n",
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" ])"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "dsVjm5CrUtXm",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"model = create_model()\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'])\n",
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"\n",
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"log_dir=\"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
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"tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
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"\n",
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"model.fit(x=x_train, \n",
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" y=y_train, \n",
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" epochs=5, \n",
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" validation_data=(x_test, y_test), \n",
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" callbacks=[tensorboard_callback])"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "TgF35qdzIC3T",
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"colab_type": "text"
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},
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"source": [
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"### Upload to TensorBoard.dev\n",
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"\n",
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"Uploading the TensorBoard logs will give a link that can be shared with anyone. Note that uploaded TensorBoards are public. Do not upload sensitive data.\n",
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"\n",
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"The uploader will keep running until it is stopped, in order to read new data from the directory during ongoing training."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "n2PvxhOkW7vn",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"!tensorboard dev upload --logdir ./logs"
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],
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"execution_count": 0,
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"outputs": []
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}
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]
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}

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