tensorflow-talk-debugging | Slides and supplementary codes | Code Inspection library

by wookayin Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | tensorflow-talk-debugging Summary

tensorflow-talk-debugging is a Python library typically used in Code Quality, Code Inspection, Tensorflow, Generative adversarial networks applications. tensorflow-talk-debugging has no vulnerabilities and it has low support. However tensorflow-talk-debugging has 1 bugs and it build file is not available. You can download it from GitHub.

My talk in the first meetup of [TensorFlow Korea Group] 18 June 2016, at Google Korea, Seoul.

Support

Quality

Security

License

Reuse

Support

tensorflow-talk-debugging has a low active ecosystem.

It has 625 star(s) with 153 fork(s). There are 41 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 3 have been closed. On average issues are closed in 7 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-talk-debugging is current.

Quality

tensorflow-talk-debugging has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 45 code smells.

Security

tensorflow-talk-debugging has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorflow-talk-debugging code analysis shows 0 unresolved vulnerabilities.

There are 15 security hotspots that need review.

License

tensorflow-talk-debugging does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

tensorflow-talk-debugging releases are not available. You will need to build from source code and install.

tensorflow-talk-debugging has no build file. You will be need to create the build yourself to build the component from source.

tensorflow-talk-debugging saves you 379 person hours of effort in developing the same functionality from scratch.

It has 904 lines of code, 46 functions and 17 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed tensorflow-talk-debugging and discovered the below as its top functions. This is intended to give you an instant insight into tensorflow-talk-debugging implemented functionality, and help decide if they suit your requirements.

Create MNIST dataset .
Train model .
Multi - layer Perceptron layer .

Get all kandi verified functions for this library.

tensorflow-talk-debugging Key Features

No Key Features are available at this moment for tensorflow-talk-debugging.

tensorflow-talk-debugging Examples and Code Snippets

No Code Snippets are available at this moment for tensorflow-talk-debugging.

Community Discussions

Trending Discussions on tensorflow-talk-debugging

Is it impossible to monitor tensorflow weight tensor in IDE(Pycharm)?

QUESTION

Is it impossible to monitor tensorflow weight tensor in IDE(Pycharm)?

Asked 2017-May-30 at 04:26

I'm trying to learn various ways to monitor tensorflow weight tensor.

I know we can watch these variable tensors through Session.run(), tf.Print(), tf.py_func()and tools like tensorboard, tdb, tfdbg (https://wookayin.github.io/tensorflow-talk-debugging/#1)

But is it impossible to use IDE(like Pycharm) for this?

I tried by myself, and couldn't find some places to set a breakpoint.

Please tell me if you succeed tensor debugging using IDE. Thank you!

...

ANSWER

Answered 2017-Mar-25 at 03:20

I debug TF daily in Pycharm. I set breakpoints as usual in the left column of the editor next to the line numbers. You can also hover over the tensors (see the yellow tooltip) to see their shape and summarized contents.

Source https://stackoverflow.com/questions/43011510

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install tensorflow-talk-debugging

You can download it from GitHub.
You can use tensorflow-talk-debugging like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: