What is the translation of " TENSORBOARD " in Chinese?

• tensorboard

Examples of using Tensorboard in English and their translations into Chinese

TensorFlow provides a utility called TensorBoard.
TensorFlow提供了一个名为TensorBoard的实用程序。

TensorBoard is a visualization tool from the TensorFlow team.
TensorBoard是Tensorflow的可视化工具。

Consider a visualization library like Tensorboard and Crayon.
考虑可视化库，例如Tensorboard和Crayon。

Use TensorBoard to visualize and understand the training process.
使用TensorBoard来直观显示并理解训练流程.

For more details, see the Summaries and TensorBoard tutorial.
更多细节请参看Summaries和TensorBoard教程。

Now TensorBoard is started and running on the default port 6006.
现在TensorBoard在默认端口6006上启动并运行。

TensorFlow also includes TensorBoard, a data visualization toolkit.
TensorFlow还包括TensorBoard，一个数据可视化工具包。

TensorBoard is potentially the most popular network visualization tool.
TensorBoard可以说是最常用的网络可视化工具。

It can be used to draw real-time learning curves, not as cumbersome as tensorboard.
它可以被用于绘制实时的学习曲线，而不像TensorBoard那样繁琐。

Tensorboard is an excellent tool for visualizing and exploring your model training.
TensorBoard是可视化和探索模型训练的一种优秀工具。

Google's What-If tool is now part of its open source TensorBoard web application.
谷歌的假设分析工具现在是其开源TensorBoardWeb应用程序的一部分。

TensorBoard shows metrics during model development and allows for making a decision regarding a model.
TensorBoard在模型开发过程中显示指标，并允许对模型做出决策。

TensorFlow has a unique diagnostic facility for its network graphs, TensorBoard.
TensorFlow为其网络图TensorBoard提供了一个独特的诊断工具。

TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs.
TensorBoard是一套Web应用程序，用于检查和理解你的TensorFlow运行和图。

The resulting policy evaluation report is exported in the training flow and can be observed using TensorBoard.
生成的策略评估报告从训练流中导出，可以使用TensorBoard进行观察。

We hope that developers use this API to extend TensorBoard and ensure that it covers a wider variety of use cases.
我们希望开发者使用此API扩展TensorBoard，并确保它能覆盖更广泛的用例。

TensorBoard is a suite of tools for graphical representation of different aspects and stages of machine learning in TensorFlow.
TensorBoard是一套工具，用于TensorFlow中机器学习的不同方面和阶段的图形表示。

We hope that developers use this API to extend TensorBoard and ensure that it covers a wider variety of use cases.
希望大家能够利用该API扩展TensorBoard并确保其能够涵盖更为广泛的用例类型。

We would like to thank the Google teams that piloted the tool and provided valuable feedback and the TensorBoard team for all their help.
我们想感谢测试此工具并提供宝贵反馈的Google团队，还要感谢TensorBoard团队的一切帮助。

Another important benefit of TensorBoard visualization is that nodes of the same types and similar structures are painted with the same colors.
TensorBoard可视化的另一个重要好处是相同类型和类似结构的节点涂有相同的颜色。

In order to log events from session which later can be used in TensorBoard, TensorFlow provides the FileWriter class.
为了从会话中记录事件，以后可以在TensorBoard中使用，TensorFlow提供了这个FileWriter类。

Now we can launch TensorBoard and see how the different models we have trained compare against each other in terms of training time and performance.
现在我们可以启动TensorBoard，看下我们刚训练的几个模型在训练时间和性能上有何不同。

Results of deep learning experiments conducted with Nauta can be seen using TensorBoard, command line code, or a Nauta web user interface.
通过TensorBoard、命令行代码或NautaWeb用户界面等方式，可以看到深度学习实验的结果。

TensorBoardX- a module for logging PyTorch models to TensorBoard, allowing developers to use the visualization tool for model training.
TensorBoardX：一个将PyTorch模型记录到TensorBoard的模块，允许开发者使用可视化工具训练模型。

TensorFlow is very accessible from Python, and includes the TensorBoard tool, which lends a strong advantage in debugging and inspecting networks.
TensorFlow用的是Python语言，它还包含了TensorBoard，这个工具在调试和检查网络方面有很大的优势。

TensorBoard is a nice visualization tool that is packaged with TensorFlow, but we can create basic charts using the matplotlib module.
TensorBoard是与TensorFlow封装在一起的出色可视化工具，不过我们可以使用matplotlib模块创建基本图表。

When we open-sourced TensorFlow in 2015, it included TensorBoard, a suite of visualizations for inspecting and understanding your TensorFlow models and runs.
当我们在2015年将TensorFlow开源时，它就包括TensorBoard了，这是一套用于检查和了解您的TensorFlow模型和运行的可视化工具。

TensorBoard reads TensorFlow event files containing summary data(observations about a model's specific operations) being generated while TensorFlow is running.
TensorBoard读取TensorFlow事件文件，其中包含在TensorFlow运行时生成的摘要数据（有关模型特定操作的观察）。

This visualization approach makes TensorBoard a popular tool for model performance evaluation, especially for models of complex structures like deep neural networks.
这种可视化方法使TensorBoard成为模型性能评估的流行工具，尤其适用于深层神经网络等复杂结构模型。

Now we can launch TensorBoard and see how the different models we have trained compare against each other in terms of training time and performance.
现在，我们可以启动TensorBoard，将我们训练出来的模型进行比较，观察它们在训练时间和性能方面都有何差异。