Name: TensorFlowの内部: サマリーとTensorBoard (Inside TensorFlow: Summaries and TensorBoard)
Uploaded: 2021-01-14T10:01:53.000Z
Duration: 43 min 23 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

And I'm here today to talk about TensorBoard and Summaries.

So first off, just an outline of what I'll be talking about.

First, I'll give an overview of TensorBoard, what it is

and how it works, just mostly sort of as background.

Then I'll talk for a bit about the tf.summary APIs.

In particular, how they've evolved from TF 1.x to TF 2.0.

about the summary data format, log directories, event files,

If you haven't, it's the visualization toolkit

That's a picture of the web UI on the right.

Typically, you run this from the command line as the TensorBoard

And from there on, you have a bunch of different controls

And the sort of key selling point of TensorBoard

is that it provides cool visualizations out of the box,

you basically can just run it on your data

and get a bunch of different kinds of tools

and different sort of analyses you can do.

So let's dive into the parts of TensorBoard

In the screenshot, it shows the scalers dashboard, which

But there's also dashboards for images, histogram, graphs,

a whole bunch more are being added every month almost.

And one thing that many of the dashboards have in common

is this ability to sort of slice and dice

And a run, you can think of that as a sign

And a tag corresponds to a specific named metric,

and evolve run on the lower left corner in the run selector.

And then we have different tags, including the cross [INAUDIBLE]

And one more thing I'll mention is that one thing a lot

So most of the data takes the form of a time series.

And in this case, with the scalers dashboard,

the time series is sort of as a step count across the x-axis.

So we might ask, what's going on behind the scenes

And so here is our architecture diagram for TensorBoard.

We'll start over on the left with your TensorFlow job.

It writes data to disk using the tf.summary API.

And we'll talk both about the summary API and the event file

Then the center component is TensorBoard itself.

We have a background thread that loads event file data.

isn't efficient for querying, we construct a subsample

of the data and memory that we can query more efficiently.

And then the rest so TensorBoard is a web server that

backend So for example, the scalers dashboard talks

to a scalers backend, images to an image backend.

And this allows the backends to do pre-processing or otherwise

And then each plugin has a frontend dashboard component,

which are all compiled together by TensorBoard

and served as a single page and index.html.

And that page communicates back and forth through the backends

And then finally, hopefully, we have our happy user

And I'll talk a little more about just some details

The front end is built on the Polymer web component

framework, where you define custom elements.

So the entirety of TensorBoard is one large custom element,

From there on, each plugin front end is--

each dashboard is its own frontend component.

For example, there's a tf-scaler dashboard.

And then all the way down to shared components

So we can think of this as a button, or a selector,

or a card element, or a collapsible pane.

字幕リスト動画再生

TensorFlowの内部: サマリーとTensorBoard (Inside TensorFlow: Summaries and TensorBoard)

specific

sort

essentially

individual