evalset | Benchmark suite of test functions | Performance Testing library

by sigopt Python Version: Current License: MIT

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

kandi X-RAY | evalset Summary

evalset is a Python library typically used in Testing, Performance Testing applications. evalset has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Benchmark suite of test functions suitable for evaluating black-box optimization strategies.

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Quality

Security

License

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Support

evalset has a low active ecosystem.

It has 39 star(s) with 10 fork(s). There are 31 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 143 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of evalset is current.

Quality

evalset has 0 bugs and 0 code smells.

Security

evalset has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

evalset code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

evalset is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

evalset releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

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evalset Key Features

No Key Features are available at this moment for evalset.

evalset Examples and Code Snippets

No Code Snippets are available at this moment for evalset.

Community Discussions

Trending Discussions on evalset

Tensorflow 2.0 Identical model structure and hyper parameters result in different performance in different calling approaches

QUESTION

Tensorflow 2.0 Identical model structure and hyper parameters result in different performance in different calling approaches

Asked 2019-Nov-28 at 07:09

there. I am a starter and learning Tensorflow 2.0. I have one model called in 3 different approaches. And the performances are different. Could anyone tell me why this is the case?

The model constructing and calling approach:

...

ANSWER

Answered 2019-Nov-28 at 07:09

the problem is solved after carefully examine the network. It turn out that the last fully connected layer in the model was activated with a relu function, which in not appropriate. And the choice of loss function tf.losses.categoricalCrossentropy and tf.nn.sparse_softmax_cross_entropy_with_logits also make a big difference. No matter what get chosen, Make sure the loss function and the final output of the network match.

Source https://stackoverflow.com/questions/59037441

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

Vulnerabilities

No vulnerabilities reported

Install evalset

You can download it from GitHub.
You can use evalset 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 .

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