qkeras | quantization deep learning library for Tensorflow Keras | Machine Learning library
kandi X-RAY | qkeras Summary
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.
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.
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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
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 09:52If 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.
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.
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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