tensorflow-exercise | Machine Learning library

by JiJingYu Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | tensorflow-exercise Summary

tensorflow-exercise is a Python library typically used in Artificial Intelligence, Machine Learning, Tensorflow applications. tensorflow-exercise has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However tensorflow-exercise build file is not available. You can download it from GitHub.

tensorflow-exercise

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tensorflow-exercise has a low active ecosystem.

It has 10 star(s) with 12 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of tensorflow-exercise is current.

Quality

tensorflow-exercise has no bugs reported.

Security

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

License

tensorflow-exercise is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

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

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

Top functions reviewed by kandi - BETA

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

Calculate the standard deviation between two images .
Compute the PSNR .
Compute the SSIM similarity between two samples .

Get all kandi verified functions for this library.

tensorflow-exercise Key Features

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

tensorflow-exercise Examples and Code Snippets

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

Community Discussions

Trending Discussions on tensorflow-exercise

pip search finds tensorflow, but pip install does not

Can't do matrix multiplication with tensorflow

QUESTION

pip search finds tensorflow, but pip install does not

Asked 2020-Jan-23 at 06:55

I am trying to build a Django app that would use Keras models to make recommendations. Right now I'm trying to use one custom container that would hold both Django and Keras. Here's the Dockerfile I've written.

...

ANSWER

Answered 2019-Jan-02 at 22:56

It looks like tensorflow only publishes wheels (and only up to 3.6), and Alpine linux is not manylinux1-compatible due to its use of musl instead of glibc. Because of this, pip cannot find a suitable installation candidate and fails. Your best options are probably to build from source or change your base image.

Source https://stackoverflow.com/questions/54014076

QUESTION

Can't do matrix multiplication with tensorflow

Asked 2017-Jun-07 at 14:11

In Tensorflow, I would like to do matrix multiplication using this code:

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ANSWER

Answered 2017-Jun-07 at 14:11

Here is the docs for tf.matmul:

Both matrices must be of the same type. The supported types are: float16, float32, float64, int32, complex64, complex128.

Changing the data type to one of the supported eliminates the error.

Source https://stackoverflow.com/questions/44414905

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

Vulnerabilities

No vulnerabilities reported

Install tensorflow-exercise

You can download it from GitHub.
You can use tensorflow-exercise 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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