tensorflow-experiments | Repository for experiments with TensorFlow | Machine Learning library

by sebastianruder C++ Version: Current License: No License

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kandi X-RAY | tensorflow-experiments Summary

tensorflow-experiments is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tensorflow-experiments has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Repository for experiments with TensorFlow

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tensorflow-experiments has a low active ecosystem.

It has 10 star(s) with 6 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

tensorflow-experiments has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-experiments is current.

Quality

tensorflow-experiments has no bugs reported.

Security

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

License

tensorflow-experiments 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.

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

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Community Discussions

Trending Discussions on tensorflow-experiments

Summary op crashes when run multiple times

QUESTION

Summary op crashes when run multiple times

Asked 2018-Jun-01 at 00:28

Opening a session for the second time and trying to run a summary op causes crash for some reason. The error message is misleading as a placeholder is clearly provided.

I am trying to test the same model on multiple datasets. So, I wanted to just reset the variables and create a new logdir for each dataset. Running them manually without a loop does the job but it is very tedious.

I tried notifying the tensorflow devs but they seemed to just ignore it. Is there a work around?

...

ANSWER

Answered 2018-Jan-03 at 02:18

Adding a tf.reset_default_graph() before with Session() as sess: fixed the problem. Tensorflow tries to use the placeholder of the last graph instead of the current graph.

Source https://stackoverflow.com/questions/48054820

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

Vulnerabilities

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