qkeras | quantization deep learning library for Tensorflow Keras | Machine Learning library

by google Python Version: 0.9.0 License: Apache-2.0

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

kandi X-RAY | qkeras Summary

qkeras is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. qkeras has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install qkeras' or download it from GitHub, PyPI.

QKeras is a quantization extension to Keras that provides drop-in replacement for some of the Keras layers, especially the ones that creates parameters and activation layers, and perform arithmetic operations, so that we can quickly create a deep quantized version of Keras network.

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qkeras has a low active ecosystem.

It has 472 star(s) with 98 fork(s). There are 31 watchers for this library.

It had no major release in the last 12 months.

There are 27 open issues and 58 have been closed. On average issues are closed in 74 days. There are 8 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of qkeras is 0.9.0

Quality

qkeras has 0 bugs and 0 code smells.

Security

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

qkeras code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

qkeras 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

qkeras releases are available to install and integrate.

Deployable package is available in PyPI.

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

Quantize a model
Add supported quantized objects
Clone a Keras model
Converts a model into a foldnorm model
Generate a random forest
Generate a random tree classifier
Convert a NumPy array to hex
Optimizes a conv2d
Calculate the padding value
Performs parallel indexing table
Calculate the number of operations for a given layer
Displays a quantized model
Call the optimizer
Generate JSON data for a model
Call the model with the given inputs
Create a quantizer
Argument parser
Compute the activation layer
Convert from binary to thermometer
Convert a dictionary to a JSON dictionary
Save quantized weights
Estimate the energy for each layer
Optimizes dense logic
Function to optimizer function
Create a pre - trained model
Build the graph

Get all kandi verified functions for this library.

qkeras Key Features

No Key Features are available at this moment for qkeras.

qkeras Examples and Code Snippets

No Code Snippets are available at this moment for qkeras.

Community Discussions

Trending Discussions on qkeras

What is the round through function in QKeras/Python?

QUESTION

What is the round through function in QKeras/Python?

Asked 2020-Nov-12 at 09:55

I was going through the QKeras implementation of the 'quantized_bits' class. Inside the call function I came across a '_round_through' function.

Here's how the function was being called:

...

ANSWER

Answered 2020-Nov-12 at 09:52

If you click on the function in GitHub, it will tell you where this function came from. In this case, the function is defined at line 271.

Source https://stackoverflow.com/questions/64801202

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

Vulnerabilities

No vulnerabilities reported

Install qkeras

You can install using 'pip install qkeras' or download it from GitHub, PyPI.
You can use qkeras 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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