self-attention-gan | Tensorflow implementation | Machine Learning library
kandi X-RAY | self-attention-gan Summary
kandi X-RAY | self-attention-gan Summary
self-attention-gan is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Generative adversarial networks applications. self-attention-gan has no vulnerabilities, it has a Permissive License and it has medium support. However self-attention-gan has 1 bugs and it build file is not available. You can download it from GitHub.
Tensorflow implementation for reproducing main results in the paper Self-Attention Generative Adversarial Networks by Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena.
Tensorflow implementation for reproducing main results in the paper Self-Attention Generative Adversarial Networks by Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena.
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Quality
Security
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self-attention-gan has a medium active ecosystem.
It has 865 star(s) with 153 fork(s). There are 37 watchers for this library.
It had no major release in the last 6 months.
There are 16 open issues and 8 have been closed. On average issues are closed in 35 days. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of self-attention-gan is current.
Quality
self-attention-gan has 1 bugs (0 blocker, 0 critical, 0 major, 1 minor) and 16 code smells.
Security
self-attention-gan has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
self-attention-gan code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
self-attention-gan is licensed under the Apache-2.0 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 releases are not available. You will need to build from source code and install.
self-attention-gan 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 saves you 571 person hours of effort in developing the same functionality from scratch.
It has 1334 lines of code, 71 functions and 9 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 and discovered the below as its top functions. This is intended to give you an instant insight into self-attention-gan implemented functionality, and help decide if they suit your requirements.
- Get real activations
- Run custominception
- Builds the model
- Build the model for a single GPU
- Helper function for training images
- Computes the grid size for the given num_images
- Get all the trainable variables
- Visualize the examples
- Merge multiple images together
- Save images to disk
- Generate test tensorflow examples
- Block block
- 2D convolutional layer
- Sample from x
- Generates examples for the examples
- A generator for the generator
- Block_no_sn
- 2d convolutional convolution layer
- Constructs a supervised model
- Run custom encoder
- Compute discriminator test
- Compute the discriminator test
- Constructs the discriminator layer
- Transformer discriminator layer
- Calculate the size of a squarest grid
- Make a random tensor
- Uses TPU
Get all kandi verified functions for this library.
self-attention-gan Key Features
No Key Features are available at this moment for self-attention-gan.
self-attention-gan Examples and Code Snippets
No Code Snippets are available at this moment for self-attention-gan.
Community Discussions
Trending Discussions on self-attention-gan
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
You can download it from GitHub.
You can use self-attention-gan 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 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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