tensorflow-cifar-10 | Cifar-10 CNN implementation using TensorFlow | Machine Learning library

by exelban Python Version: v1.0.1 License: MIT

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kandi X-RAY | tensorflow-cifar-10 Summary

tensorflow-cifar-10 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tensorflow-cifar-10 has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However tensorflow-cifar-10 build file is not available. You can download it from GitHub.

Cifar-10 CNN implementation using TensorFlow library with 20% error.

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tensorflow-cifar-10 has a low active ecosystem.

It has 86 star(s) with 48 fork(s). There are 4 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 5 have been closed. On average issues are closed in 9 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-cifar-10 is v1.0.1

Quality

tensorflow-cifar-10 has 0 bugs and 0 code smells.

Security

tensorflow-cifar-10 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorflow-cifar-10 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tensorflow-cifar-10 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

tensorflow-cifar-10 releases are available to install and integrate.

tensorflow-cifar-10 has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

tensorflow-cifar-10 saves you 107 person hours of effort in developing the same functionality from scratch.

It has 272 lines of code, 10 functions and 4 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed tensorflow-cifar-10 and discovered the below as its top functions. This is intended to give you an instant insight into tensorflow-cifar-10 implemented functionality, and help decide if they suit your requirements.

Get the cifar_10 dataset
Download and extract zipar_10
Convert a dense tensor
Train the model
Runs the test and saves accuracy
Calculate learning rate
Generate the model

Get all kandi verified functions for this library.

tensorflow-cifar-10 Key Features

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tensorflow-cifar-10 Examples and Code Snippets

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Community Discussions

Trending Discussions on tensorflow-cifar-10

Out Of Memory when running multi-gpu cnn with TensorFlow

QUESTION

Out Of Memory when running multi-gpu cnn with TensorFlow

Asked 2019-May-24 at 16:49

I'm trying to run a simple cnn on cifar10, combining code from 2 examples: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_cnn.py

https://github.com/exelban/tensorflow-cifar-10

I'm getting OOM errors.

I first tried the code with the complete cnn , without multi-gpu support, and it is working ok. Next I used the multi-gpu code, ran ok too. Combining them is not working.

...

ANSWER

Answered 2019-May-24 at 16:49

Here's the solution: The problem was with how the data was divided across the GPUs. I used tf.split(X, _NUM_GPUS) for the data and the labels, then I could assign each GPU with it's right data chunk. Also , only one GPU is running accuracy so it needed to get the full sized data.

Source https://stackoverflow.com/questions/56266133

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

Vulnerabilities

No vulnerabilities reported

Install tensorflow-cifar-10

You can download it from GitHub.
You can use tensorflow-cifar-10 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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