infogan | Keras implementation of InfoGAN | Machine Learning library

by EmilienDupont Python Version: Current License: No License

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kandi X-RAY | infogan Summary

infogan is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. infogan has no bugs, it has no vulnerabilities and it has low support. However infogan build file is not available. You can download it from GitHub.

Keras implementation of InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets by Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel.

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infogan has a low active ecosystem.

It has 11 star(s) with 4 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 58 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of infogan is current.

Quality

infogan has no bugs reported.

Security

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

License

infogan 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.

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

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

Top functions reviewed by kandi - BETA

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

Plot a figure of a grid .
Initialize the model .
Train the discriminator .
Setup the generator .
Train discriminator on fake data .
Process the MNIST dataset .
Computes the mutual info loss of the discriminator .
Generate a filename for a timestamp .
Samples a categorical .
Generate a random unit Gaussian .

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infogan Key Features

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infogan Examples and Code Snippets

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Community Discussions

Trending Discussions on infogan

Change Model input_shape but got an : ValueError: Input 0 of layer dense_44 is incompatible with the layer

When training GANs in Keras, are multiple passes required to optimize the generator and discriminator?

Problems with reshape in GAN's discriminator (Tensorflow)

QUESTION

Change Model input_shape but got an : ValueError: Input 0 of layer dense_44 is incompatible with the layer

Asked 2020-Oct-06 at 13:47

I am new to python and DL. Please help me to correct the error. This class was originly created with mnist dataset (28 x 28) I tried to adapt it to my work and the image that I am using are (224 x 224). I changed the input image shape but still have the incompatible shape image and the model still use the old shapes of mnist. Knowng that the that I am using: X_train=(676, 224, 224)/y_train(676,)/X_test(170, 224, 224)/y_test(170,)

The code :

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ANSWER

Answered 2020-Oct-06 at 13:47

You forgot to change the architecture of the generator. The generator's output shape and the discriminator's input shape have to match. That's what causing the error.

To fix it, you need to fix the architecture. The generator produces images in shape (28, 28, 1), but you want (224, 224, 1). The shape the architecture produces is the result of the architecture itself and its parameters.

So I added two Upsampling layers and changed the size of the other layers to match the discriminator's output.

Also, I removed ZeroPadding2D layer from discriminator, since it made the shape odd (15, 15, ..), and therefore it was impossible to match the same size in the generator.

Here's the code:

Source https://stackoverflow.com/questions/64217263

QUESTION

When training GANs in Keras, are multiple passes required to optimize the generator and discriminator?

Asked 2019-Jul-15 at 22:48

I'm more familiar with tensorflow graph training than Keras, but I'm trying out Keras here.

In building a GAN the generator needs be optimized against a different loss than the discriminator (the opposite loss). In base tensorflow this is easy enough to implement using either 2 optimizers or by calling optimizer.compute_gradients(...) and optimizer.apply_gradients(...) separately with the appropriate group of weights.

In Keras, I don't see that I can achieve either of these. In implementations such as Keras-GAN, it appears that the training of generator and discriminator are split into separate models and then trained independently batch-by-batch. This means many more passes are required per effective update than would be required with the base tensorflow implementation with two optimizers operating on one pass.

Is there a way to implement the optimizer for GANs so that both generator and discriminator get trained in one pass in Keras?

TF 1.14

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ANSWER

Answered 2019-Jul-15 at 22:48

This is a really tough question for Keras for several reasons:

A model can only have one optimizer... it would be necessary to change the source code for it to accept two or more
Even when you are using a custom optimizer, it would be possible to separate the weights, but it doesn't offer support to separate the losses, as can be seen in the source code for optimizers. The probability is that the optimizer already computes a final common loss (which would then make it impossible to attribute one loss for a group of weights and another for the other group)
The training mechanisms are not easy to find in the code. Things are spread all around, supporting many things such as loss weights, sample weights, etc. The time that it would take to summarize everything and then decide what to do/change would be too much.

Answer suggestion

Make your model in Keras as you would. The discriminator, the generator, their connections and outputs.

Just don't compile it. Instead, keep track of the main tensors (generator output, discriminator output, generator input), create the loss functions in Tensorflow style and train everything in tensorflow style.

Source https://stackoverflow.com/questions/56961681

QUESTION

Problems with reshape in GAN's discriminator (Tensorflow)

Asked 2017-Jun-22 at 10:19

I was trying to implement various GANs in Tensorflow (after doing it successfully in PyTorch), and I am having some problems while coding the discriminator part.

The code of the discriminator (very similar to the MNIST CNN tutorial) is:

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ANSWER

Answered 2017-Jun-21 at 09:59

Instead of passing None to reshape you must pass -1.

So this:

Source https://stackoverflow.com/questions/44672766

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

Vulnerabilities

No vulnerabilities reported

Install infogan

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

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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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