GAN-MNIST | Generative Adversarial Network for MNIST with tensorflow | Machine Learning library
kandi X-RAY | GAN-MNIST Summary
kandi X-RAY | GAN-MNIST Summary
GAN-MNIST is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Generative adversarial networks applications. GAN-MNIST has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However GAN-MNIST build file is not available. You can download it from GitHub.
Generative Adversarial Network for MNIST with tensorflow
Generative Adversarial Network for MNIST with tensorflow
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Quality
Security
License
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GAN-MNIST has a low active ecosystem.
It has 192 star(s) with 71 fork(s). There are 15 watchers for this library.
It had no major release in the last 6 months.
There are 3 open issues and 2 have been closed. On average issues are closed in 0 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of GAN-MNIST is current.
Quality
GAN-MNIST has 0 bugs and 99 code smells.
Security
GAN-MNIST has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
GAN-MNIST code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
GAN-MNIST 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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GAN-MNIST releases are not available. You will need to build from source code and install.
GAN-MNIST 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.
GAN-MNIST saves you 321 person hours of effort in developing the same functionality from scratch.
It has 770 lines of code, 33 functions and 10 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed GAN-MNIST and discovered the below as its top functions. This is intended to give you an instant insight into GAN-MNIST implemented functionality, and help decide if they suit your requirements.
- Build the model
- Generate the graph
- Compute the discriminator
- Lrelu layer
- Compute bce
- Generates samples
- Batch normalization
- Randomize the mnist with validation set
- Generate numpy array
- Resize image
- Saves the visualization
- One hot method
Get all kandi verified functions for this library.
GAN-MNIST Key Features
No Key Features are available at this moment for GAN-MNIST.
GAN-MNIST Examples and Code Snippets
No Code Snippets are available at this moment for GAN-MNIST.
Community Discussions
Trending Discussions on GAN-MNIST
QUESTION
Can't instantiate a Keras model when batch_normalization is used
Asked 2019-Jun-08 at 17:15
I am not sure what I am doing wrong but I am following the code from a book to create a GAN model, and during instantiation the Python shell is just freezing. The code is actually a subset of some code from a book, but the book code also fails to create a model.
If I comment out the batch_norm however I can instantiate
a model.
Here:
...ANSWER
Answered 2019-Jun-08 at 17:15I tried your code in google colab. The following is generated. I think it's not a problem of the code. You may check other problem, e.g. setting.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install GAN-MNIST
You can download it from GitHub.
You can use GAN-MNIST 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 GAN-MNIST 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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