Category Archives: TensorFlow

[ 翻譯 ] 2018.09.21- 認識TensorFlow.js,在瀏覽器就能用JavaScript 訓練機器學習模型喔!

原文作者

Josh Gordon  Sara Robinson

圖片

Josh Gordon、Sara Robinson提供

翻譯宗諭審閱阿吉老師
說明

感謝兩位作者的授權翻譯,特此致謝!原文請見這裡

We’re excited to introduce TensorFlow.js, an open-source library you can use to define, train, and run machine learning models entirely in the browser, using Javascript and a high-level layers API. If you’re a Javascript developer who’s new to ML, TensorFlow.js is a great way to begin learning. Or, if you’re a ML developer who’s new to Javascript, read on to learn more about new opportunities for in-browser ML. In this post, we’ll give you a quick overview of TensorFlow.js, and getting started resources you can use to try it out.

 

我們很興奮能向大家介紹TensorFlow.js。TensorFlow.js是一個完全開放原始碼的函式庫,只要運用Javascript與其高階API,就能在瀏覽器中直接定義、訓練並運行機器學習模型。若您本身是一位Javascript開發者,但對機器學習不是那麼熟悉,那TensorFlow.js是一個很好的學習方式。又或者,您是一位對Javascript不太熟的機器學習開發者,請繼續閱讀本文以幫助您深入了解更多關於瀏覽器內機器學習的新契機。在這篇文章中,我們將快速介紹TensorFlow.js,還有您可用來嘗試TensorFlow.js的快速上手資源。

 

In-Browser ML

Running machine learning programs entirely client-side in the browser unlocks new opportunities, like interactive ML! If you’re watching the livestream for the TensorFlow Developer Summit, during the TensorFlow.js talk you’ll find a demo where @dsmilkov and @nsthorat train a model to control a PAC-MAN game using computer vision and a webcam, entirely in the browser. You can try it out yourself, too, with the link below — and find the source in the examples folder.

 

瀏覽器內的機器學習

在瀏覽器客戶端就能直接執行機器學習程式能帶出許多新契機,例如互動式的機器學習!您可以看看TensorFlow開發者大會直播,在關於TensorFlow.js的分享中有一段是@dsmilkov@nsthorat 正demo一個完全跑在瀏覽器中的範例,運用電腦視覺和網路攝影機訓練一個模型以控制小精靈遊戲。您也可以使用下面的連結親自試一下,並在examples資料夾中找到原始程式碼。

 

If you’d like to try another game, give the Emoji Scavenger Hunt a whirl — this time, from a browser on your mobile phone.

 

如果想試試看其他遊戲,可以試試看Emoji Scavenger Hunt ,但這次請使用您手機上的瀏覽器嘗試。

圖1

 

ML running in the browser means that from a user’s perspective, there’s no need to install any libraries or drivers. Just open a webpage, and your program is ready to run. In addition, it’s ready to run with GPU acceleration. TensorFlow.js automatically supports WebGL, and will accelerate your code behind the scenes when a GPU is available. Users may also open your webpage from a mobile device, in which case your model can take advantage of sensor data, say from a gyroscope or accelerometer. Finally, all data stays on the client, making TensorFlow.js useful for low-latency inference, as well as for privacy preserving applications.

 

由使用者的角度來說,在瀏覽器中就能執行機器學習代表不用再安裝任何函式庫或驅動程式了。只要打開網頁就可以執行程式了。此外,它還可支援GPU加速。TensorFlow.js會自動支援WebGL,當GPU可用的時候,它會在背景中加速您的程式。使用者也可透過行動裝置開啟網頁,這樣一來您的模型就能取用手機的感測器資料,例如陀螺儀或是加速度計。最後,所有資料皆保留在客戶端上,使TensorFlow.js既適用於低延遲推論,也適用於需要保護的應用程式。

 

What can you do with TensorFlow.js?

If you’re developing with TensorFlow.js, here are three workflows you can consider.

  • You can import an existing, pre-trained model for inference. If you have an existing TensorFlow or Kerasmodel you’ve previously trained offline, you can convert into TensorFlow.js format, and load it into the browser for inference.

 

  • You can re-train an imported model. As in the Pac-Man demo above, you can use transfer learning to augment an existing model trained offline using a small amount of data collected in the browser using a technique called Image Retraining. This is one way to train an accurate model quickly, using only a small amount of data.

 

  • Author models directly in browser. You can also use TensorFlow.js to define, train, and run models entirely in the browser using Javascript and a high-level layers API. If you’re familiar with Keras, the high-level layers API should feel familiar.

 

我們可以運用TensorFlow.js做些什麼?

若您正使用TensorFlow.js進行開發,以下三個工作流程是您可考慮的:

  • 您可匯入現有、預先訓練好的模型進行推論。若您有一個現有的TensorFlow或Keras模型,您可將其轉換為js的檔案格式,並將其載入至瀏覽器中進行推論。

 

  • 您可以重新訓練一個已匯入的模型。在前面提及的小精靈遊戲範例中,您可使用遷移式學習,透過使用一種叫做「影像再訓練(Image Retraining)」的技術,運用在瀏覽器中收集到的少量數據,做到在離線狀態下也能訓練現有模型。這是一種只需少量資料也能快速訓練精確模型的方法。

 

  • 直接在瀏覽器中建立模型。您也可以用TensorFlow.js,運用Javascript和高階API,直接在瀏覽器內定義、訓練並運作模型。若您熟悉Keras,那麼這些高層API對您來說應該不陌生。

 

Let’s see some code

If you like, you can head directly to the samples or tutorials to get started. These show how-to export a model defined in Python for inference in the browser, as well as how to define and train models entirely in Javascript. As a quick preview, here’s a snippet of code that defines a neural network to classify flowers, much like on the getting started guide on TensorFlow.org. Here, we’ll define a model using a stack of layers.

 

一起來看看程式碼

若您願意,您可直接使用範例教程開始學習。它們展示了如何匯出用Python定義的模型並在瀏覽器中進行推論,還有如何單單使用Javascript定義並訓練模型。很快提一下,以下的程式碼,它定義了一個可以分類花朵的神經網絡,很類似TensorFlow.org上的入門教學。在此用到了多個層來定義模型。

import * as tf from ‘@tensorflow/tfjs’;
const model = tf.sequential();
model.add(tf.layers.dense({inputShape: [4], units: 100}));
model.add(tf.layers.dense({units: 4}));
model.compile({loss: ‘categoricalCrossentropy’, optimizer: ‘sgd’});

 

The layers API we’re using here supports all of the Keras layers found in the examples directory (including Dense, CNN, LSTM, and so on). We can then train our model using the same Keras-compatible API with a method call:

 

這個layers API支援範例目錄中所有的Keras層(包括Dense、CNN、LSTM等等)。接著,只要呼叫對應的方法就可以使用相容於Keras的API訓練模型。以下是這個方法:

await model.fit(
  xData, yData, {
    batchSize: batchSize,
    epochs: epochs
});

 

The model is now ready to use to make predictions:

這個模型已經可以進行預測了:

// Get measurements for a new flower to generate a prediction
// The first argument is the data, and the second is the shape.
const inputData = tf.tensor2d([[4.8, 3.0, 1.4, 0.1]], [1, 4]);

// Get the highest confidence prediction from our model
const result = model.predict(inputData);
const winner = irisClasses[result.argMax().dataSync()[0]];

// Display the winner
console.log(winner);

 

TensorFlow.js also includes a low-level API (previously deeplearn.js) and support for Eager execution. You can learn more about these by watching the talk at the TensorFlow Developer Summit.

 

TensorFlow.js也包括一個低階的API(前身是deeplearn.js),並且支援Eager execution。請參考TensorFlow開發者大會的內容,您就能更深入了解。

圖2 TensorFlow.js API的概述

 

How does TensorFlow.js relate to deeplearn.js?

Good question! TensorFlow.js, an ecosystem of JavaScript tools for machine learning, is the successor to deeplearn.js which is now called TensorFlow.js Core. TensorFlow.js also includes a Layers API, which is a higher level library for building machine learning models that uses Core, as well as tools for automatically porting TensorFlow SavedModels and Keras hdf5 models. For answers to more questions like this, check out the FAQ.

 

TensorFlow.js與deeplearn.js的關聯是什麼?

這是一個好問題!TensorFlow.js是一個為了機器學習而誕生的JavaScript工具生態系,是deeplearn.js的繼承者,而deeplearn.js現在則被稱為TensorFlow.js Core。TensorFlow.js還包括一個Layers API,這是一個更高階的函式庫,能運用Core來建置機器學習模型,它也是一款自動移植TensorFlow SavedModels與Keras hdf5模型的工具。更多類似問題的答案,請參閱FAQ

 

Where’s the best place to learn more?

To learn more about TensorFlow.js, visit the project homepage, check out the tutorials, and try the examples. You can also watch the talk from the 2018 TensorFlow Developer Summit, and follow TensorFlow on Twitter.

 

哪裡最適合學習TensorFlow.js呢?

想更深入了解TensorFlow.js,請到本專案的首頁查看教學,玩玩看各個範例。您還可觀看2018年TensorFlow開發者大會的演講,並在Twitter上關注TensorFlow

 

Thanks for reading, and we’re excited to see what you’ll create with TensorFlow.js! If you like, you can follow @dsmilkov@nsthorat, and @sqcaifrom the TensorFlow.js team on Twitter for updates.

 

謝謝您耐心讀完本文,我們很期待看到您運用TensorFlow.js做出的成果!喜歡的話,可以在Twitter上關注TensorFlow.js團隊的@dsmilkov@nsthorat@ sqcai 等人的帳號來獲得最新消息。

 

備註:若您想購買AI人工智慧相關產品,請洽機器人王國商城,謝謝。

 

相關文章

[ 翻譯 ] 2018.08.03-根據TensorFlow團隊,TensorFlow 1.9正式支援樹莓派

原文作者

Pete Warden

圖片

CAVEDU教育團隊

翻譯宗諭審閱吉弘
說明

感謝作者Pete Warden的授權翻譯,特此致謝!

When TensorFlow was first launched in 2015, we wanted it to be an “open source machine learning framework for everyone”. To do that, we need to run on as many of the platforms that people are using as possible. We’ve long supported Linux, MacOS, Windows, iOS, and Android, but despite the heroic efforts of many contributors, running TensorFlow on a Raspberry Pi has involved a lot of work. Thanks to a collaboration with the Raspberry Pi Foundation, we’re now happy to say that the latest 1.9 release of TensorFlow can be installed from pre-built binaries using Python’s pip package system! If you’re running Raspbian 9 (stretch), you can install it by running these two commands from a terminal:

當TensorFlow於2015年首次發佈時,我們希望它是一個「給所有人的開源機器學習框架」。爲要達成這一點,我們需要盡可能讓它在更多人們使用的平台上運作,所以,我們長期以來支援了Linux、MacOS、Windows、iOS及Android等作業系統。然而,儘管許多貢獻者已經貢獻卓越,在Raspberry Pi上運行TensorFlow仍有許多工作需要完成。感謝與Raspberry Pi基金會的合作,我們現在很高興能宣佈:最新的TensorFlow 1.9版,可透過Python的pip套件系統來安裝建置好的二元檔了!若您正使用Raspbian 9(Raspbian stretch),只要從終端機輸入下面這兩道指令來安裝它:

 

sudo apt install libatlas-base-dev
pip3 install tensorflow

 

You can then run python3 in a terminal, and use TensorFlow just as you would on any other platform. Here’s a simple hello world example:

接下來,您可在終端機上運作python3,並如同在任何其它平台上一樣使用TensorFlow。下面是一個簡單的hello world範例:

 

# Python
import tensorflow as tf
tf.enable_eager_execution()
hello = tf.constant(‘Hello, TensorFlow!’)
print(hello)

 

If the system outputs the following, then you are ready to begin writing TensorFlow programs:

Hello, TensorFlow!

如果看到以下內容:Hello, TensorFlow!, 那您便可開始編寫TensorFlow的程式。

 

There are more details on installing and troubleshooting TensorFlow on the Raspberry Pi on the TensorFlow website.

更多細節,請參考TensorFlow官網上的內容

 

We’re excited about this because the Raspberry Pi is used by many innovative developers, and is also widely used in education to introduce people to programming, so making TensorFlow easier to install will help open up machine learning to new audiences. We’ve already seen platforms like DonkeyCar use TensorFlow and the Raspberry Pi to create self-driving toy cars, and we can’t wait to discover what new projects will be built now that we’ve reduced the difficulty.

我們對這件事感到很興奮,因為Raspberry Pi早已被許多創新開發者使用著,也被廣泛應用於教育領域來推廣程式教育。所以,讓TensorFlow更容易安裝於Raspberry Pi上,將有助於新朋友認識何謂機器學習。我們已經看到像 DonkeyCar 這樣的平台使用TensorFlow與Raspberry Pi來做出能夠自動駕駛的玩具小車。我們也迫不及待想知道,在使用難度降低之後,會有哪些新專案誕生。

 

Eben Upton, founder of the Raspberry Pi project, says, “It is vital that a modern computing education covers both fundamentals and forward-looking topics. With this in mind, we’re very excited to be working with Google to bring TensorFlow machine learning to the Raspberry Pi platform. We’re looking forward to seeing what fun applications kids (of all ages) create with it,” and we agree!

Raspberry Pi創辦人Eben Upton表示:「現今的電腦程式教育必須涵蓋基礎知識與前瞻性的主題。考慮到這一點,我們非常興奮能與Google合作,將TensorFlow機器學習引入Raspberry Pi平台。我們期待看見所有年齡層的孩子們能運用它來打造各種有趣的應用。」這,我們完全同意!

 

We’re hoping to see a lot more educational material and tutorials emerge that will help more and more people explore the possibilities of machine learning on such a cost-effective and flexible device.

我們深切盼望看見更多教材和教學資源問世,幫助更多人在Raspberry Pi這款高CP值又具備擴充彈性的裝置上探索機器學習的各種可能性。

 

備註:若您想要購買樹莓派,可以前往機器人王國商城,謝謝。

 

相關文章

[翻譯] 2018.8.14-根據TensorFlow團隊,TensorFlow 2.0預覽版預計於下半年發佈(繁、簡中文版)

原文作者

Martin Wicke

發佈時間

原文發佈日期:2018年8月14日

翻譯宗諭審閱吉弘
說明

轉載自TensorFlow團隊社群內的公開公告,非常感謝TensorFlow團隊的授權翻譯,特此致謝!

Since the open-source release in 2015, TensorFlow has become the world’s most widely adopted machine learning framework, catering to users and use-cases. In this time, TensorFlow has evolved along with rapid developments in computing hardware, machine learning research, and commercial deployment.

 

自從TensorFlow於2015年以開放原始碼形式發佈後,已成為全球最廣為採用的機器學習框架,提供給廣泛的使用者及案例。時至今日,TensorFlow也隨著運算硬體、機器學習研究及商用部署的快速發展而不斷進化。

 

自从TensorFlow于2015年以开放原始码形式发布后,已成为全球最广为采用的机器学习框架,提供给广泛的使用者及案例。时至今日,TensorFlow也随着运算硬体、机器学习研究及商用部署的快速发展而不断进化。

 

Reflecting these rapid changes, we have started work on the next major version of TensorFlow. TensorFlow 2.0 will be a major milestone, with a focus on ease of use. Here are some highlights of what users can expect with TensorFlow 2.0:

  • Eager execution will be a central feature of 2.0. It aligns users’ expectations about the programming model better with TensorFlow practice and should make TensorFlow easier to learn and apply.
  • Support for more platforms and languages, and improved compatibility and parity between these components via standardization on exchange formats and alignment of APIs.
  • We will remove deprecated APIs and reduce the amount of duplication, which has caused confusion for users.

 

爲回應這股快速變遷的趨勢,我們(TensorFlow團隊)已開始研發TensorFlow下一個主要版本。TensorFlow 2.0將會是重要的里程碑,著眼於讓大家更容易使用。以下是一些使用者可期盼的焦點:

  • Eager execution將會是0版本的中心特色,它能滿足使用者對於實做更棒的程式模型的期待,並且應該會使TensorFlow更易於學習及應用。
  • 支援更多的平台及語言,亦改善了相容性,並且透過交換格式及API調整的標準化,在這些元件中取得平衡。
  • 我們將會移除已棄用的API,並且降低重複性,避免造成使用者混淆。

 

为回应这股快速变迁的趋势,我们(TensorFlow团队)已开始研发TensorFlow下一个主要版本。 TensorFlow 2.0将会是重要的里程碑,着眼于让大家更容易使用。以下是一些使用者可期盼的焦点:

  • Eager execution将会是0版本的中心特色,它能满足使用者对于实做更棒的程式模型的期待,并且应该会使TensorFlow更易于学习及应用。
  • 支援更多的平台及语言,亦改善了相容性,并且透过交换格式及API调整的标准化,在这些元件中取得平衡。
  • 我们将会移除已弃用的API,并且降低重复性,避免造成使用者混淆。

 

We are planning to release a preview version of TensorFlow 2.0 later this year.

 

我們預計於2018年下半年發佈TensorFlow 2.0的預覽版本。

 

我们预计于2018年下半年发布TensorFlow 2.0的预览版本。

 

Public 2.0 design process

Shortly, we will hold a series of public design reviews covering the planned changes. This process will clarify the features that will be part of TensorFlow 2.0, and allow the community to propose changes and voice concerns. Please join developers@tensorflow.org if you would like to see announcements of reviews and updates on process. We hope to gather user feedback on the planned changes once we release a preview version later this year.

 

公開的 TensorFlow 2.0設計程序

簡而言之,我們將針對已在計劃中的更新有一系列的「公開設計評論」。這個過程會說明那些即將納入TensorFlow 2.0中的功能,並且允許社群提出修改且表達關切。若您想得知評論的公告與設計過程的進展,請加入developers@tensorflow.org。一旦預覽版本於2018年下半年發佈之後,我們期盼收到使用者對於計畫中的更新的回饋意見。

 

公开的 TensorFlow 2.0设计程序

简而言之,我们将针对已在计划中的更新有一系列的「公开设计评论」。这个过程会说明那些即将纳入TensorFlow 2.0中的功能,并且允许社群提出修改且表达关切。若您想得知评论的公告与设计过程的进展,请加入developers@tensorflow.org。一旦预览版本于2018年下半年发布之后,我们期盼收到使用者对于计画中的更新的回馈意见。

 

Compatibility and continuity

TensorFlow 2.0 is an opportunity to correct mistakes and to make improvements which are otherwise forbidden under semantic versioning.

 

相容性與連續性

TensorFlow 2.0的發佈是一個改正錯誤的好機會,並且針對在semantic versioning下被禁止的部分作出改善。

 

相容性与连续性

TensorFlow 2.0的发布是一个改正错误的好机会,并且针对在semantic versioning下被禁止的部分作出改善。

 

To ease the transition, we will create a conversion tool which updates Python code to use TensorFlow 2.0 compatible APIs, or warns in cases where such a conversion is not possible automatically. A similar tool has helped tremendously in the transition to 1.0.

 

為減緩過渡時期的衝擊,我們將提供一個轉換工具,它具備兩項功能:(1)更新Python程式碼得以使用相容於TensorFlow 2.0的API;(2)假如轉換無法完全自動完成時,將發出警告。類似的工具在當年轉換到Tensorflow1.0版時可真是幫了大忙呢。

 

为减缓过渡时期的冲击,我们将提供一个转换工具,它具备两项功能:(1)更新Python程式码得以使用相容于TensorFlow 2.0的API;(2)假如转换无法完全自动完成时,将发出警告。类似的工具在当年转换到Tensorflow1.0版时可真是帮了大忙呢。

 

Not all changes can be made fully automatically. For example, we will be deprecating APIs, some of which do not have a direct equivalent. For such cases, we will offer a compatibility module (tensorflow.compat.v1) which contains the full TensorFlow 1.x API, and which will be maintained through the lifetime of TensorFlow 2.x.

 

並非所有的更新皆可全部自動完成。例如以棄用API來說,有些將要被棄用的API並沒有一個直接可對應的應用程式去處理。針對這樣的情況,我們將提供一套相容性模組(tensorflow.compat.v1),包含完整的TensorFlow 1.x API,並且確保它在TensorFlow 2.0的產品週期中被維護。

 

并非所有的更新皆可全部自动完成。例如以弃用API来说,有些将要被弃用的API并没有一个直接可对应的应用程式去处理。针对这样的情况,我们将提供一套相容性模组(tensorflow.compat.v1),包含完整的TensorFlow 1.x API,并且确保它在TensorFlow 2.0的产品周期中被维护。

 

We do not anticipate any further feature development on TensorFlow 1.x once a final version of TensorFlow 2.0 is released. We will continue to issue security patches for the last TensorFlow 1.x release for one year after TensorFlow 2.0’s release date.

 

一旦TensorFlow 2.0最終版本發佈後,就不會在開發任何TensorFlow 1.x的功能了。我們將在TensorFlow 2.0發佈一年之內,針對TensorFlow 1.x的最新版持續提供安全性修正程式(Security Patch)。

 

一旦TensorFlow 2.0最终版本发布后,就不会在开发任何TensorFlow 1.x的功能了。我们将在TensorFlow 2.0发布一年之内,针对TensorFlow 1.x的最新版持续提供安全性修正程式(Security Patch)。

 

On-disk compatibility

We do not intend to make breaking changes to SavedModels or stored GraphDefs (i.e., we plan to include all current kernels in 2.0). However, the changes in 2.0 will mean that variable names in raw checkpoints might have to be converted before being compatible with new models.

 

儲存的相容性

我們無意針對SavedModels或儲存的GraphDefs做出大幅度的修改。(例如,我們想在TensorFlow 2.0中納入所有現行的核心。)然而,在TensorFlow 2.0中的更新代表在原始檢查點中的變數名稱,在順利相容於新模型前可能需要進行轉換。

 

储存的相容性

我们无意针对SavedModels或储存的GraphDefs做出大幅度的修改。 (例如,我们想在TensorFlow 2.0中纳入所有现行的核心。)然而,在TensorFlow 2.0中的更新代表在原始检查点中的变数名称,在顺利相容于新模型前可能需要进行转换。

 

tf.contrib

TensorFlow’s contrib module has grown beyond what can be maintained and supported in a single repository. Larger projects are better maintained separately, while we will incubate smaller extensions along with the main TensorFlow code. Consequently, as part of releasing TensorFlow 2.0, we will stop distributing tf.contrib. We will work with the respective owners on detailed migration plans in the coming months, including how to publicise your TensorFlow extension in our community pages and documentation. For each of the contrib modules we will either a) integrate the project into TensorFlow; b) move it to a separate repository or c) remove it entirely. This does mean that all of tf.contrib will be deprecated, and we will stop adding new tf.contrib projects today. We are looking for owners/maintainers for a number of projects currently in tf.contrib, please contact us (reply to this email) if you are interested.

 

tf.contrib

TensorFlow的contrib模組的規模已超過可在單一版本庫中維護並支援的程度。較大的專案最好是獨立維護,同時我們會讓較小的擴充檔去跟著TensorFlow主線去走。結論是,作為發佈TensorFlow 2.0過程的一部分,我們將停止發佈tf.contrib。我們會在這幾個月之內針對個別擁有者去敲定遷移的細節,包括如何在我們的社群網頁和文件中宣傳您的TensorFlow擴充。針對每一個補充支援模組,我們將採取以下三項措施中的一項:

(a)整合專案進TensorFlow中;

(b)移至另外的版本庫,或

(c)將它整個移除。

這代表所有的tf.contrib未來都不會再使用,並且從今天起就不再允許加入新的補充支援模組專案。我們對目前tf.contrib的某些專案正在徵求維護者,如果您有興趣的話請和我們連繫(announce@tensorflow.org)。

 

tf.contrib

TensorFlow的contrib模组的规模已超过可在单一版本库中维护并支援的程度。较大的专案最好是独立维护,同时我们会让较小的扩充档去跟着TensorFlow主线去走。结论是,作为发布TensorFlow 2.0过程的一部分,我们将停止发布tf.contrib。我们会在这几个月之内针对个别拥有者去敲定迁移的细节,包括如何在我们的社群网页和文件中宣传您的TensorFlow扩充。针对每一个补充支援模组,我们将采取以下三项措施中的一项:

(a)整合专案进TensorFlow中;

(b)移至另外的版本库,或

(c)将它整个移除。

这代表所有的tf.contrib未来都不会再使用,并且从今天起就不再允许加入新的补充支援模组专案。我们对目前tf.contrib的某些专案正在征求维护者,如果您有兴趣的话请和我们连系(announce@tensorflow.org)。

 

Next steps

For questions about development of or migration to TensorFlow 2.0, contact us at discuss@tensorflow.org. To stay up to date with the details of 2.0 development, please subscribe to developers@tensorflow.org, and participate in related design reviews.

 

下一步

針對TensorFlow 2.0的開發或轉的的相關問題,請來信discuss@tensorflow.org 詢問。若想要得知TensorFlow 2.0的最新進度,請訂閱developers@tensorflow.org ,並且參與相關的設計評論,謝謝。

 

下一步

针对TensorFlow 2.0的开发或转的的相关问题,请来信discuss@tensorflow.org 询问。若想要得知TensorFlow 2.0的最新进度,请订阅developers@tensorflow.org ,并且参与相关的设计评论,谢谢。

 

On behalf of the TensorFlow team,

Martin

 

謹代表全體TensorFlow團隊

Martin

 

谨代表全体TensorFlow团队

Martin

 

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