progressive_growing_of_gans | Progressive Growing of GANs for Improved Quality, Stability, and Variation | Machine Learning library

by tkarras Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | progressive_growing_of_gans Summary

progressive_growing_of_gans is a Python library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. progressive_growing_of_gans has no vulnerabilities and it has medium support. However progressive_growing_of_gans has 1 bugs, it build file is not available and it has a Non-SPDX License. You can download it from GitHub.

Progressive Growing of GANs for Improved Quality, Stability, and Variation

Support

Quality

Security

License

Reuse

Support

progressive_growing_of_gans has a medium active ecosystem.

It has 5929 star(s) with 1100 fork(s). There are 273 watchers for this library.

It had no major release in the last 6 months.

progressive_growing_of_gans has no issues reported. There are 11 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of progressive_growing_of_gans is current.

Quality

progressive_growing_of_gans has 1 bugs (1 blocker, 0 critical, 0 major, 0 minor) and 112 code smells.

Security

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

progressive_growing_of_gans code analysis shows 0 unresolved vulnerabilities.

There are 9 security hotspots that need review.

License

progressive_growing_of_gans has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

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

progressive_growing_of_gans 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.

progressive_growing_of_gans saves you 1242 person hours of effort in developing the same functionality from scratch.

It has 2794 lines of code, 229 functions and 15 files.

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

Top functions reviewed by kandi - BETA

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

Create CelebA
Process multiple items concurrently
Add an image to the TensorBoard
Shuffle the image
Train the discriminator
Imports a module
Loads a tfrecord dataset
Find an object in a module
Create CIFAR - 10 images
Loads a CIFAR - 100 training dataset
Create MNIST dataset
Create a TFRecord from images
Loads images from svn files
Create LSUN dataset
Calculate fid value for given paths
Generate fake images
Create a MNIST image
Gets the loss of the WAN
Generate interpolation video
Generate a paper
Calculate the WAN
Evaluate metrics
Generates a paper for the LaTeX paper
Execute the command line
Run the network
Generate training video

Get all kandi verified functions for this library.

progressive_growing_of_gans Key Features

No Key Features are available at this moment for progressive_growing_of_gans.

progressive_growing_of_gans Examples and Code Snippets

No Code Snippets are available at this moment for progressive_growing_of_gans.

Community Discussions

Trending Discussions on progressive_growing_of_gans

Tensorflow what is the tf.contrib.nccl.allsum in new version?

Tensorflow can't assign a device for operation

TensorFlow Operation and cannot be found in official API

QUESTION

Tensorflow what is the tf.contrib.nccl.allsum in new version?

Asked 2020-Feb-29 at 10:16

It seems that from tensorflow 1.13, there is no api such as tf.contrib.nccl.allsum. However, in the Nvidia official GitHub https://github.com/tkarras/progressive_growing_of_gans, which uses this old API to reduce sum from different gpu devices as the following.

...

ANSWER

Answered 2020-Feb-29 at 10:16

I think the same API is nccl_ops.all_sum. I have demoed this API by the following code.

Source https://stackoverflow.com/questions/60453533

QUESTION

Tensorflow can't assign a device for operation

Asked 2019-Apr-01 at 16:10

I am trying to run NVidia's face generating demo on my computer. I am using Windows 10. I have downloaded the source, and am trying to follow the steps further down the page. I have installed the latest NVidia drivers for my GTX1060, which should be a device that supports cuda functionality. I have installed the Cuda Toolkit, as well as the cuDNN SDK that TensorFlow requires.

However, when running the import_example.py script I am getting the below error. Can anyone tell me what I am doing wrong?

...

ANSWER

Answered 2019-Apr-01 at 16:10

Cannot assign a device for operation G_paper_1/Run/G_paper_1/latents_in: {{node G_paper_1/Run/G_paper_1/latents_in}}was explicitly assigned to /device:GPU:0 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0 ]

have you installed tensorflow or tensorflow-gpu? The later is the one you want if you want to use your GPU.

It might also be a version compatibility issue. First, check if you have your nvidia driver installed with: nvidia-smi, you should get something like this:

Source https://stackoverflow.com/questions/55248631

QUESTION

TensorFlow Operation and cannot be found in official API

Asked 2018-Dec-17 at 14:32

recently I try to repeat and learn the code posted on GitHub by Nvidia--progressive_growing_of_gans. However, I find that there are several operations that I can not find reference based on official API as the following.

...

ANSWER

Answered 2018-Dec-17 at 14:32

The shape attribute of a tf.Tensor object is a tf.TensorShape object. As you can see in the documentation, ndims is the number of dimensions or "rank" of the tensor (or None, if the shape is fully dynamic).

The op attribute is the tf.Operation that produces the tensor. In this, inputs is the list of tensors that are received by the operation. So:

Source https://stackoverflow.com/questions/53816414

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

Vulnerabilities

No vulnerabilities reported

Install progressive_growing_of_gans

You can download it from GitHub.
You can use progressive_growing_of_gans 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 .

Find more information at: