tgan-pytorch | PyTorch implementation of Temporal Generative Adversarial | Machine Learning library

by proceduralia Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | tgan-pytorch Summary

tgan-pytorch is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. tgan-pytorch has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However tgan-pytorch build file is not available. You can download it from GitHub.

A PyTorch implementation of Temporal Generative Adversarial Nets with Singular Value Clipping

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tgan-pytorch has a low active ecosystem.

It has 10 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 2 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tgan-pytorch is current.

Quality

tgan-pytorch has 0 bugs and 2 code smells.

Security

tgan-pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tgan-pytorch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tgan-pytorch is licensed under the MIT License. This license is Permissive.

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

Reuse

tgan-pytorch releases are not available. You will need to build from source code and install.

tgan-pytorch 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.

It has 266 lines of code, 13 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Calculates the gradient of the gradient .
Initialize the model .
Compute the activation matrix .
Generate random input .
Get an item from the dataset .
Returns the length of the data .

Get all kandi verified functions for this library.

tgan-pytorch Key Features

No Key Features are available at this moment for tgan-pytorch.

tgan-pytorch Examples and Code Snippets

No Code Snippets are available at this moment for tgan-pytorch.

Community Discussions

Trending Discussions on tgan-pytorch

FastGAN - RuntimeError: Error(s) in loading state_dict for Generator

QUESTION

FastGAN - RuntimeError: Error(s) in loading state_dict for Generator

Asked 2021-Oct-19 at 01:37

I'm running FastGAN (https://github.com/odegeasslbc/FastGAN-pytorch) on Google Colab and now trying to resume training from a saved .pth generated by the network. However, it keeps throwing this error:

...

ANSWER

Answered 2021-Oct-07 at 10:30

This is common when changing the attribute name of the submodules in your nn.Module.

Notice how most of your layer keys here differ from the ones contained in the loaded state dict because of their prefix: all keys in the dictionary have a 'module.' prefix.

A quick fix you be to slice away this prefix. You could for instance usea dict comprehension:

Source https://stackoverflow.com/questions/69478457

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

Vulnerabilities

No vulnerabilities reported

Install tgan-pytorch

You can download it from GitHub.
You can use tgan-pytorch 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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