GANs | PyTorch implementation of several GANs | Machine Learning library

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kandi X-RAY | GANs Summary

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

PyTorch implementation of several GANs.

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GANs has a low active ecosystem.

It has 38 star(s) with 14 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

GANs has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of GANs is current.

Quality

GANs has 0 bugs and 0 code smells.

Security

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

GANs code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

GANs does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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

GANs has no build file. You will be need to create the build yourself to build the component from source.

GANs saves you 245 person hours of effort in developing the same functionality from scratch.

It has 596 lines of code, 30 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 GANs and discovered the below as its top functions. This is intended to give you an instant insight into GANs implemented functionality, and help decide if they suit your requirements.

Train the model
Generates a random sample of z
Convert samples to matplotlib figure

Get all kandi verified functions for this library.

GANs Key Features

No Key Features are available at this moment for GANs.

GANs Examples and Code Snippets

No Code Snippets are available at this moment for GANs.

Community Discussions

Trending Discussions on GANs

GANs output is not optimum

absl.flags Error: Trying to access flag before flags were parsed

Hyperparameter tuning for GANs using Grid Search

DCGANs discriminator accuracy metric using PyTorch

How do I define the split_train_test in Python?

Unflatten a tensor back to an image

My PyTorch GAN is changing from producing random noise to darkness with no convergence. Why is this?

Correct way to apply Minibatch Standard Deviation to Keras GAN layer

PyTorch expected CPU got CUDA tensor

How can I do data augmentation with _OptionsDataset?

QUESTION

GANs output is not optimum

Asked 2021-Jun-07 at 17:59

I am trying to implement GANs for super-resolution enhancement using Tensorflow. Here is the link of my collab code https://colab.research.google.com/drive/1F376Y6T5ehoE-mk2q7LGBmLq-OkWxqtr?usp=sharing

First image is the output image

Second is the Expected output

Can someone help me figure out what is wrong in the image. Any help would be appreciated. Thank you

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ANSWER

Answered 2021-Jun-07 at 17:59

Actually, your generator looks quite simple. Try adding some more convolutional layers Conv2D after upsampling the inputs. There are also many techniques & architectures (such as Pix2Pix) that handle very well high quality images.

This repository contains quite a few GANs that might help you improve your model + some tricks You can do to improve the overall quality: https://github.com/kochlisGit/Keras-GAN

Source https://stackoverflow.com/questions/66784369

QUESTION

absl.flags Error: Trying to access flag before flags were parsed

Asked 2021-Apr-23 at 14:04

I'm trying to parse this flag 'dataroot' (string type).

Consider this as a demo code:

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ANSWER

Answered 2021-Apr-23 at 14:04

So, I solved this by doing this:

Source https://stackoverflow.com/questions/66953343

QUESTION

Hyperparameter tuning for GANs using Grid Search

Asked 2021-Mar-20 at 03:11

I am working on GANs right now. And the main problem I am facing that is tuning the hyperparameters. This is one of the big problem for GANs. As I research about hyperparameters tuning I found the name Grid Searching. So, I want to use this grid search for tuning the Hyperparameter's of GANs. I don't understand how to introduce this. If any one have the idea about this please help. Or any better idea about tuning the Hyperparameter's of GANs then share.

Thank You.

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ANSWER

Answered 2021-Mar-20 at 03:11

You can use the following kera function : https://www.tensorflow.org/tutorials/keras/keras_tuner

You can specify which hyper parameter you want to test, their range of values, etc. You can test different learning rates, number of layers, activation functions and more.

You can also look at the following sklearn package: https://machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/

This being said, hyper parameter tuning is pretty expensive, especially for GANs which are already hard to train, as you said.

It might be better to start the training on a smaller subset of the data to get a good idea of the hyper parameters to use and then run hyper parameter tuning on a smaller subset of hyper parameters.

Source https://stackoverflow.com/questions/66670796

QUESTION

DCGANs discriminator accuracy metric using PyTorch

Asked 2021-Feb-25 at 10:51

I am implementing DCGANs using PyTorch.

It works well in that I can get reasonable quality generated images, however now I want to evaluate the health of the GAN models by using metrics, mainly the ones introduced by this guide https://machinelearningmastery.com/practical-guide-to-gan-failure-modes/

Their implementation uses Keras which SDK lets you define what metrics you want when you compile the model, see https://keras.io/api/models/model/. In this case the accuracy of the discriminator, i.e. percentage of when it successfully identifies an image as real or generated.

With the PyTorch SDK, I can't seem to find a similar feature that would help me easily acquire this metric from my model.

Does Pytorch provide the functionality to be able to define and extract common metrics from a model?

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ANSWER

Answered 2021-Feb-25 at 10:51

Pure PyTorch does not provide metrics out of the box, but it is very easy to define those yourself.

Also there is no such thing as "extracting metrics from model". Metrics are metrics, they measure (in this case accuracy of discriminator), they are not inherent to the model.

Binary accuracy

In your case, you are looking for binary accuracy metric. Below code works with either logits (unnormalized probability outputed by discriminator, probably last nn.Linear layer without activation) or probabilities (last nn.Linear followed by sigmoid activation):

Source https://stackoverflow.com/questions/66365566

QUESTION

How do I define the split_train_test in Python?

Asked 2021-Jan-24 at 08:17

I am working on a python code to generate network traffic using GANs and I'm getting this error that split_train_test is not defined. I have imported train_test_split from sklearn.model_selection but it doesnt seem to work. What am I not doing right? This is the error message;

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ANSWER

Answered 2021-Jan-24 at 08:17

It should be train_test_split

Reference the docs

Source https://stackoverflow.com/questions/65868345

QUESTION

Unflatten a tensor back to an image

Asked 2020-Dec-23 at 01:05

I am working on GANs and I want to visualize the image formed.

For this, I was trying

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ANSWER

Answered 2020-Dec-22 at 10:28

You need to call cpu() before broadcasting with view.

Source https://stackoverflow.com/questions/65406713

QUESTION

My PyTorch GAN is changing from producing random noise to darkness with no convergence. Why is this?

Asked 2020-Dec-17 at 10:19

My code is very basic, just following the first lab from DeepLearning.ai's GAN specialization. However, my code does not have the same output, what is the reason for this. Sorry if this is just a silly mistake, this is my first experience with GANs. I begin by creating the Generator and Discriminator classes, my random noise function, and creating my models. I then run the training loop, but after 3 epochs, all of the outputs from the GAN are black.

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ANSWER

Answered 2020-Dec-17 at 10:19

Your discriminator loss is wrong. The labels for the real images should be 1 instead of 0.

Updated code:

Source https://stackoverflow.com/questions/65333797

QUESTION

Correct way to apply Minibatch Standard Deviation to Keras GAN layer

Asked 2020-Dec-04 at 04:01

I'm trying to improve the stability of my GAN model by adding a standard deviation variable to my layer's feature map. I'm following the example set in the GANs-in-Action git. The math itself makes sense to me. The mechanics of my model and the reasons why this addresses mode collapse makes sense to me. However, a shortcoming from the example is that they never actually show how this code is executed.

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ANSWER

Answered 2020-Jul-30 at 17:56

this is my proposal...

the problem is related to the minibatch_std_layer function. first of all your network deals with 3d data while the original minibatch_std_layer deals with 4d data so you need to adapt it. secondly, the input variable defined in this function is unknown (also in the source code you cited) so I think the most obvious and logical solution is to consider it as the layer variable (the input of minibatch_std_layer). with this in mind the modified minibatch_std_layer becomes:

Source https://stackoverflow.com/questions/63122680

QUESTION

PyTorch expected CPU got CUDA tensor

Asked 2020-Oct-02 at 07:57

I've been struggling to find what's wrong in my code. I'm trying to implement DCGAN paper and from the past 1 hour, I'm going through these errors. Could anyone please help me fix this?

I'm training this on Google colab with GPU runtime but I'm getting this error. Yesterday, I implemented the first GAN paper by Ian Goodfellow and I did not got this error. I don't know what's happening any help would be appreciated. Also, please check whether the gen_input is correct or not.

Here is the code:

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ANSWER

Answered 2020-Sep-30 at 14:06

if torch.cuda.is_available():
    generator = generator.cuda()

Source https://stackoverflow.com/questions/64139164

QUESTION

How can I do data augmentation with _OptionsDataset?

Asked 2020-Sep-05 at 03:27

Hello I am learning about GANS and deep learning, and in general, when I worked with that I did with NumPy arrays of images, but for this homework, I get the data with tfds of the following way:

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ANSWER

Answered 2020-Sep-05 at 03:27

tf.image has a bunch of random transformations you can use, you don't need Numpy. Here's an example. I had to select the splits a little differently since I have another version. Here is the documentation for tf.image.

Source https://stackoverflow.com/questions/63749327

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

Vulnerabilities

No vulnerabilities reported

Install GANs

You can download it from GitHub.
You can use 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 .

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