pytorch_resnet_cifar10 | Proper implementation of ResNet-s for CIFAR10/100 | Computer Vision library

by akamaster Python Version: Current License: BSD-2-Clause

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kandi X-RAY | pytorch_resnet_cifar10 Summary

pytorch_resnet_cifar10 is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. pytorch_resnet_cifar10 has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However pytorch_resnet_cifar10 build file is not available. You can download it from GitHub.

Proper implementation of ResNet-s for CIFAR10/100 in pytorch that matches description of the original paper.

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pytorch_resnet_cifar10 has a medium active ecosystem.

It has 961 star(s) with 302 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 24 have been closed. On average issues are closed in 155 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pytorch_resnet_cifar10 is current.

Quality

pytorch_resnet_cifar10 has 0 bugs and 0 code smells.

Security

pytorch_resnet_cifar10 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pytorch_resnet_cifar10 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pytorch_resnet_cifar10 is licensed under the BSD-2-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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pytorch_resnet_cifar10 releases are not available. You will need to build from source code and install.

pytorch_resnet_cifar10 has no build file. You will be need to create the build yourself to build the component from source.

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

pytorch_resnet_cifar10 saves you 124 person hours of effort in developing the same functionality from scratch.

It has 319 lines of code, 23 functions and 3 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed pytorch_resnet_cifar10 and discovered the below as its top functions. This is intended to give you an instant insight into pytorch_resnet_cifar10 implemented functionality, and help decide if they suit your requirements.

Train the model
Compute accuracy
Update statistics
Validate the loss function
Takes a network and prints the total number of params
Save checkpoint to file

Get all kandi verified functions for this library.

pytorch_resnet_cifar10 Key Features

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pytorch_resnet_cifar10 Examples and Code Snippets

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Community Discussions

Trending Discussions on pytorch_resnet_cifar10

Does PyTorch loss() and backpropagation understand lambda layers?

QUESTION

Does PyTorch loss() and backpropagation understand lambda layers?

Asked 2020-Jan-24 at 07:21

I've been working with a resnet56 model from the code provided here: https://github.com/akamaster/pytorch_resnet_cifar10/blob/master/resnet.py.

I noticed that the implementation is different from many of the other available ResNet examples online, and I was wondering if PyTorch's backpropagation algorithm using loss() can account for the lambda layer and shortcut in the code provided.

If that is the case, can anyone provide insight into how PyTorch is able to interpret the lambda layer for backpropagation (i.e. how does PyTorch know how to differentiate with respect to the layer's operations)?

P.S. I also had to modify the code to fit my own use-case, and it seems like my own implementation with option == 'A' does not produce great results. This may simply be because option == 'B,' which uses convolutional layers instead of padding, is better for my data.

...

ANSWER

Answered 2020-Jan-24 at 07:21

"I was wondering if PyTorch's backpropagation algorithm using loss() can account for the lambda layer and shortcut in the code provided."

PyTorch has no problem with backpropagating through lambda functions. Your LambdaLayer is just defining the forward pass of the Module as the evaluation of the lambda function, so your question boils down to whether PyTorch can backpropagate through lambda functions.

"If that is the case, can anyone provide insight into how PyTorch is able to interpret the lambda layer for backpropagation (i.e. how does PyTorch know how to differentiate with respect to the layer's operations)?"

The lambda function performs the torch.nn.functional.Pad function on x, which we can packpropagate through because it is has a defined backwards() function.

PyTorch handles lambda functions the same way an autodiff tool like PyTorch handles any function: it breaks it up into primitive operations, and uses the differentiation rules for each primitive operation to build up the derivative of the entire computation.

Source https://stackoverflow.com/questions/59815491

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Vulnerabilities

No vulnerabilities reported

Install pytorch_resnet_cifar10

You can download it from GitHub.
You can use pytorch_resnet_cifar10 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.

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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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