ResNet_cifar | Tensorflowr ResNet cifar10 and cifar100 blog：http : //blog

by chaipangpang Python Version: Current License: No License

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

kandi X-RAY | ResNet_cifar Summary

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

Tensorflowr ResNet cifar10 and cifar100 blog：

Support

Quality

Security

License

Reuse

Support

ResNet_cifar has a low active ecosystem.

It has 84 star(s) with 62 fork(s). There are 3 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 1007 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ResNet_cifar is current.

Quality

ResNet_cifar has 0 bugs and 0 code smells.

Security

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

ResNet_cifar code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ResNet_cifar 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

ResNet_cifar releases are not available. You will need to build from source code and install.

ResNet_cifar has no build file. You will be need to create the build yourself to build the component from source.

ResNet_cifar saves you 175 person hours of effort in developing the same functionality from scratch.

It has 434 lines of code, 20 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Evaluate the model
Build an input tensor
Connects the model
Batch normalization
Build the train op
Basic convolution layer
Build the graph
Return fully connected tensor
The weight decay function
Calculate global average pool
Reduce tensor
Convert stride to array
Train the model
Bottleneck bottleneck
Residual residual

Get all kandi verified functions for this library.

ResNet_cifar Key Features

No Key Features are available at this moment for ResNet_cifar.

ResNet_cifar Examples and Code Snippets

No Code Snippets are available at this moment for ResNet_cifar.

Community Discussions

No Community Discussions are available at this moment for ResNet_cifar.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install ResNet_cifar

You can download it from GitHub.
You can use ResNet_cifar 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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