ConvolutionalAutoEncoder | A very simple convolutional autoencoder | Machine Learning library

by ToniCreswell Python Version: Current License: No License

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kandi X-RAY | ConvolutionalAutoEncoder Summary

ConvolutionalAutoEncoder is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. ConvolutionalAutoEncoder has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

A very simple convolutional autoencoder implemented in lasagne, applied to MNIST as an example.

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ConvolutionalAutoEncoder has a low active ecosystem.

It has 16 star(s) with 4 fork(s). There are 2 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 833 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of ConvolutionalAutoEncoder is current.

Quality

ConvolutionalAutoEncoder has 0 bugs and 0 code smells.

Security

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

ConvolutionalAutoEncoder code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ConvolutionalAutoEncoder 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

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

Build file is available. You can build the component from source.

ConvolutionalAutoEncoder saves you 40 person hours of effort in developing the same functionality from scratch.

It has 108 lines of code, 6 functions and 1 files.

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

Top functions reviewed by kandi - BETA

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

Train the Gaussian model .
Prepare the network .
Builds the network .
get the arguments
Load MNIST dataset .
Test if x is a predicate function .

Get all kandi verified functions for this library.

ConvolutionalAutoEncoder Key Features

No Key Features are available at this moment for ConvolutionalAutoEncoder.

ConvolutionalAutoEncoder Examples and Code Snippets

No Code Snippets are available at this moment for ConvolutionalAutoEncoder.

Community Discussions

Trending Discussions on ConvolutionalAutoEncoder

Error while running Convolutional Autoencoder RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

QUESTION

Error while running Convolutional Autoencoder RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

Asked 2018-Nov-03 at 14:18

I am a noob and am creating a model in PyTorch for the first time. I am trying to create a convolutional autoencoder and am getting the error while running the model. The code I am using is:

...

ANSWER

Answered 2018-Nov-03 at 14:18

I cannot test your model, but considering the error message it makes sense the cause of your problem lies in the return value of your forward.

Currently you are returning x which is your actual input not the output:

Source https://stackoverflow.com/questions/53128752

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

Vulnerabilities

No vulnerabilities reported

Install ConvolutionalAutoEncoder

You can download it from GitHub.
You can use ConvolutionalAutoEncoder 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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