ibp_vae | mean-field and structured VAEs for the IBP

by rachtsingh Python Version: Current License: No License

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

kandi X-RAY | ibp_vae Summary

ibp_vae is a Python library. ibp_vae has no bugs, it has no vulnerabilities and it has low support. However ibp_vae build file is not available. You can download it from GitHub.

We're working on incorporating all of the required CUDA functionality in PyTorch master, but until then the compilation process is likely a little tricky. For example, you'll almost definitely need to change sm_35 in make.sh to your card's compute capability and the CUDA_PATH must be customized. Finally, one likely change you'll need to make is to fix build.py to have the correct cuda_path and include_dirs. Please feel free to send me an email if you have trouble compiling - I'm happy to help.

Support

Quality

Security

License

Reuse

Support

ibp_vae has a low active ecosystem.

It has 22 star(s) with 10 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. On average issues are closed in 402 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ibp_vae is current.

Quality

ibp_vae has no bugs reported.

Security

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

License

ibp_vae 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

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

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

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

Top functions reviewed by kandi - BETA

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

Train the model
NLL and KL function
Evaluate an ELB model
Print out the training epoch
Compute the value of the model
Encodes the given tensor
Decode the covariance matrix
Reparametrize a discrete logistic model
Evaluate the loss function
Reparametrize matrices
Sample from a covariance matrix
Compute the logarithm of an array
Calculate control variables
Decode the covariance
Feed loss function
Download fixed binarized data
Check if the file exists
Generate training data
Compute the logarithm of a and b
Calculate samples from b
Saves checkpoint to filename
Gradient of the gradients
Calculate the IWAE loss
Calculate the control variables
Compute the KL divergence
Evaluate the model
Backward computation

Get all kandi verified functions for this library.

ibp_vae Key Features

No Key Features are available at this moment for ibp_vae.

ibp_vae Examples and Code Snippets

No Code Snippets are available at this moment for ibp_vae.

Community Discussions

No Community Discussions are available at this moment for ibp_vae.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install ibp_vae

To run the code with GPU support, navigate to src/lgamma and do ./make.sh. Navigate to base directory and run. --savefile is a required argument. Models will be saved under models/ (last, and best over epochs) and train, valid, test curves and timings are saved under runs/. Use --help to see arguments.

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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