normalizing_flows | Pytorch implementations of density estimation algorithms | Machine Learning library

by kamenbliznashki Python Version: Current License: No License

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kandi X-RAY | normalizing_flows Summary

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

Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows

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normalizing_flows has a low active ecosystem.

It has 547 star(s) with 97 fork(s). There are 16 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 7 have been closed. On average issues are closed in 21 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of normalizing_flows is current.

Quality

normalizing_flows has 0 bugs and 0 code smells.

Security

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

normalizing_flows code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

normalizing_flows 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.

Top functions reviewed by kandi - BETA

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

Download images from Celaba
Extract a single tar archive
Calculates the checksum of a file
Download files from Google Drive
Optimization of flow function
Plot flow density
Plots the flow in the given base distribution
Plot the target density
Visualize a model
Fetch the dataset for a dataset
Train and evaluate a model
Plot a sample distribution and density
Fetch all datasets
Logit function
Train a model
R Plot the flow density function
Plot the marginal histogram of the data
Plot marginalized histograms
Plot marginal histograms
Load training and validation and test data
Load data normalized to be normalized
Plot a sample distribution and plot it
Plots the target density
Fetch data from numpy arrays
Evaluate the model
Generate a model
Loads the data as a numpy array
Plot a tensorflow model
Plots the flow density of a flow
Plots the flow of the given base distribution

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normalizing_flows Key Features

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normalizing_flows Examples and Code Snippets

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Community Discussions

Trending Discussions on normalizing_flows

Error message when training CNF example from Julia documentation

QUESTION

Error message when training CNF example from Julia documentation

Asked 2020-Oct-09 at 21:08

I'm new with Julia trying to run the example proposed at https://diffeqflux.sciml.ai/stable/examples/normalizing_flows/ to define and train a continuous normalizing flow using sciml_train.

I just copy/pasted the written code and gets the following error:

...

ANSWER

Answered 2020-Oct-09 at 21:08

Given, the error message seems a little bit cryptic, due to issuing "#5#7" instead of a proper function name for the method which is seemingly not callable for the two input arguments of types ::Array{Float32,1}, ::Float32.

This is presumably due to some not properly defined variable/symbol, which is assumed by a caller to be callable like a function. The caller probably seems to be stemming from within the code you are using.

The thing which seems like the most probable source of this error seems to me to be the cb in res1 = DiffEqFlux.sciml_train(loss_adjoint, ffjord_test.p, ADAM(0.1), cb = cb, maxiters = 100) . It is meant to be shorthand for "callback" and assumed to be a function defined by the user, and is called from within sciml_train. Comparing with the linked document where you took the example from, I can verify that your snippet matches the code over there. I haven't tested it out myself by now, but could you check that the error is gone as soon as you either define a cb function or remove it from the function call? If that'd be the case, there'd be an error within the official documentation.

Source https://stackoverflow.com/questions/64283160

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Vulnerabilities

No vulnerabilities reported

Install normalizing_flows

You can download it from GitHub.
You can use normalizing_flows 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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