DeepLearningExamples | Deep learning examples with Python and Tensorflow | Machine Learning library

by mrtkp9993 Python Version: v1.0 License: GPL-3.0

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kandi X-RAY | DeepLearningExamples Summary

DeepLearningExamples is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. DeepLearningExamples has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Deep learning examples with Python.

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DeepLearningExamples has a low active ecosystem.

It has 17 star(s) with 3 fork(s). There are 3 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DeepLearningExamples is v1.0

Quality

DeepLearningExamples has no bugs reported.

Security

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

License

DeepLearningExamples is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

DeepLearningExamples releases are available to install and integrate.

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

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

Top functions reviewed by kandi - BETA

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

Performs a training step .
Initialize autoencoder .
Call keras .

Get all kandi verified functions for this library.

DeepLearningExamples Key Features

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DeepLearningExamples Examples and Code Snippets

No Code Snippets are available at this moment for DeepLearningExamples.

Community Discussions

Trending Discussions on DeepLearningExamples

How to run NVIDIA DeepLearningExamples for Resnet50v1.5 using int8

QUESTION

How to run NVIDIA DeepLearningExamples for Resnet50v1.5 using int8

Asked 2020-Sep-17 at 07:06

NVIDIA DeepLearningExamples provide inference benchmark tests for Resnet50v1.5 in float32 and float16 precision but not in int8, so is there anyone that has tried running these tests in int8 before? Any tips and suggestions for how to do it would be really appreciated.

Update: I am trying to test with int8, but is currently stuck at tf.layers.conv2d complaining about type mismatch since it does not accept integer types.

...

ANSWER

Answered 2020-Sep-17 at 07:06

For anyone who is interested, there would be some layers that required to be updated in order to accept integer types. There are some options available:

Write your own custom layers (e.g. tf.layers.conv2d) to accept integer inputs.
Use tf.cast to cast inputs and outputs between float and integer types.
Export the model to some formats (e.g. SavedModel) and use some conversion tool (e.g. TensorRT, TensorFlow Lite) to convert the model to int8.

Since I need the layers themselves to be optimized for int8 but do not want to spend time implementing them by myself, I have chosen the last option.

Source https://stackoverflow.com/questions/63912917

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

Vulnerabilities

No vulnerabilities reported

Install DeepLearningExamples

You can download it from GitHub.
You can use DeepLearningExamples 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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