Self-Attention-GAN-Tensorflow | Simple Tensorflow implementation | Machine Learning library
kandi X-RAY | Self-Attention-GAN-Tensorflow Summary
kandi X-RAY | Self-Attention-GAN-Tensorflow Summary
Self-Attention-GAN-Tensorflow is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Generative adversarial networks applications. Self-Attention-GAN-Tensorflow has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Self-Attention-GAN-Tensorflow build file is not available. You can download it from GitHub.
Simple Tensorflow implementation of "Self-Attention Generative Adversarial Networks" (SAGAN)
Simple Tensorflow implementation of "Self-Attention Generative Adversarial Networks" (SAGAN)
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Self-Attention-GAN-Tensorflow has a low active ecosystem.
It has 513 star(s) with 148 fork(s). There are 14 watchers for this library.
It had no major release in the last 6 months.
There are 16 open issues and 11 have been closed. On average issues are closed in 75 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Self-Attention-GAN-Tensorflow is current.
Quality
Self-Attention-GAN-Tensorflow has 0 bugs and 0 code smells.
Security
Self-Attention-GAN-Tensorflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Self-Attention-GAN-Tensorflow code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Self-Attention-GAN-Tensorflow is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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Self-Attention-GAN-Tensorflow releases are not available. You will need to build from source code and install.
Self-Attention-GAN-Tensorflow 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.
Self-Attention-GAN-Tensorflow saves you 288 person hours of effort in developing the same functionality from scratch.
It has 696 lines of code, 55 functions and 5 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed Self-Attention-GAN-Tensorflow and discovered the below as its top functions. This is intended to give you an instant insight into Self-Attention-GAN-Tensorflow implemented functionality, and help decide if they suit your requirements.
- Train the model
- Merge two images
- Load model from checkpoint_dir
- Save the model to checkpoint_dir
- Build the model
- Convolution layer
- Compute discriminator layer
- Google attention layer
- Parse arguments
- Check args
- Check if log_dir exists
- A convolutional layer
- Test the model
- Download Celeb - A
- Download a file from Google Drive
- Save the content of the response
- Return confirm token from response cookies
- Attention layer
- Generate images for a given epoch
- Load dataset
- Load MNIST dataset
- Load cifar10 dataset
- Resize image
Get all kandi verified functions for this library.
Self-Attention-GAN-Tensorflow Key Features
No Key Features are available at this moment for Self-Attention-GAN-Tensorflow.
Self-Attention-GAN-Tensorflow Examples and Code Snippets
No Code Snippets are available at this moment for Self-Attention-GAN-Tensorflow.
Community Discussions
Trending Discussions on Self-Attention-GAN-Tensorflow
QUESTION
Keras.backend.reshape: TypeError: Failed to convert object of type to Tensor. Consider casting elements to a supported type
Asked 2020-Mar-25 at 07:29
I'm designing a custom layer for my neural network, but I get an error from my code.
I want to do a attention layer as described in the paper: SAGAN. And the original tf code
...ANSWER
Answered 2018-Jun-12 at 20:46You are accessing the tensor's .shape property which gives you Dimension objects and not actually the shape values. You have 2 options:
- If you know the shape and it's fixed at layer creation time you can use
K.int_shape(x)[0]which will give the value as an integer. It will however returnNoneif the shape is unknown at creation time; for example if the batch_size is unknown. - If shape will be determined at runtime then you can use
K.shape(x)[0]which will return a symbolic tensor that will hold the shape value at runtime.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Self-Attention-GAN-Tensorflow
You can download it from GitHub.
You can use Self-Attention-GAN-Tensorflow 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.
You can use Self-Attention-GAN-Tensorflow 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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