flow-gan | Combining Maximum Likelihood and Adversarial Learning | Machine Learning library

by ermongroup Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | flow-gan Summary

flow-gan is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. flow-gan has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models.

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Quality

Security

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flow-gan has a low active ecosystem.

It has 90 star(s) with 26 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 147 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of flow-gan is current.

Quality

flow-gan has 0 bugs and 0 code smells.

Security

flow-gan has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

flow-gan code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

flow-gan is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

flow-gan releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

flow-gan saves you 618 person hours of effort in developing the same functionality from scratch.

It has 1439 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 flow-gan and discovered the below as its top functions. This is intended to give you an instant insight into flow-gan implemented functionality, and help decide if they suit your requirements.

Train the model
Evaluate negative log likelihood
Calculate inception and mode score
Calculate exposure and mode score
Calculate the forward and Jacobian
Layer L_M
Get weights
Simple batch norm
Build the model
Layer normalization
2D convolutional layer
Discriminator layer
Load a cifar dataset
Download and extract a Cifar - 10 dataset
Unpickles data from a file
Generate a model specification for a given model
Construct a nice nic specification
Construct the model spec
Get an image
Read image file
Backward computation
Computes the Jacobian and Jacobian
Compute the forward and Jacobian
Backward compatibility
Performs the forward and Jacobian
Concatenate tensor

Get all kandi verified functions for this library.

flow-gan Key Features

No Key Features are available at this moment for flow-gan.

flow-gan Examples and Code Snippets

No Code Snippets are available at this moment for flow-gan.

Community Discussions

Trending Discussions on flow-gan

Why do I get a BufferOverflowException when running a TensorFlowLite Model?

Cant import Tensorflow 2.2.0rc2 in Google Colab when installed from setup.py

QUESTION

Why do I get a BufferOverflowException when running a TensorFlowLite Model?

Asked 2020-Sep-08 at 05:32

I want to run a custom tflite model on Android using TensorFlowLite (and using Kotlin). Despite using the TFLite support library to create a supposedly correctly shaped input and output buffer I get the following error message everytime I'm calling my run() method.

Here is my class:

...

ANSWER

Answered 2020-Sep-08 at 05:32

Does adding .rewind() to your input and output buffer make it work? If not, I wonder if your input or output tensor is dynamic tensor? In which case the return shape is not usable this way.

Source https://stackoverflow.com/questions/63742300

QUESTION

Cant import Tensorflow 2.2.0rc2 in Google Colab when installed from setup.py

Asked 2020-Mar-31 at 11:25

Im trying to import the latest rc2 version of Tensorflow (2.2.0rc2 at this date) in Google Colab, but cant do it when installed from my setup.py install script.

When i install Tensorflow manually using !pip install tensorflow==2.2.0rc2 from a Colab cell, everything is ok and im able to import Tensorflow.

The next is how i have my dependencies installation setup in Google Colab:

...

ANSWER

Answered 2020-Mar-30 at 18:31

I found a work around, but this is not the solution to this problem by far, so this will not be accepted as solution, but will help people in same trouble to keep going with their work:

Install your requirements manually before installing your custom package, in my case, this is pip install -r "/content/deep-deblurring/requirements.txt":

Source https://stackoverflow.com/questions/60936032

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

Vulnerabilities

No vulnerabilities reported

Install flow-gan

You can download it from GitHub.
You can use flow-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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