quantized-rram-net | Quantized training method for RRAM-based systems

by qingyangqing Python Version: Current License: No License

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

kandi X-RAY | quantized-rram-net Summary

quantized-rram-net is a Python library. quantized-rram-net has no bugs, it has no vulnerabilities and it has low support. However quantized-rram-net build file is not available. You can download it from GitHub.

Quantized training method for RRAM-based systems.

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Support

quantized-rram-net has a low active ecosystem.

It has 9 star(s) with 0 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 1009 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of quantized-rram-net is current.

Quality

quantized-rram-net has no bugs reported.

Security

quantized-rram-net has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

quantized-rram-net does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

quantized-rram-net releases are not available. You will need to build from source code and install.

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

Runs inference on images
Create a variable with weight decay
Summarize activations
Create a variable on CPU
Reads images from cifar10 files
Reads a CIFAR - 10 dataset
Create a batch of images and labels
Reads input files
Train the loss function
Adds summaries for all losses
Calculate the L2 loss
Download and extract a CIFAR - 10 file
Quantize weights
Quantize a tensor
Calculate the mean and standard deviation of weights
Quantize the weights
Returns a list of all trainable variables

Get all kandi verified functions for this library.

quantized-rram-net Key Features

No Key Features are available at this moment for quantized-rram-net.

quantized-rram-net Examples and Code Snippets

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Community Discussions

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

Vulnerabilities

No vulnerabilities reported

Install quantized-rram-net

You can download it from GitHub.
You can use quantized-rram-net 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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