logsumexp | Fast SSE logsumexp for python/numpy | Computer Vision library

by rmcgibbo C Version: Current License: BSD-2-Clause

X-Ray Key Features Code Snippets Community Discussions(6)Vulnerabilities Install Support

kandi X-RAY | logsumexp Summary

logsumexp is a C library typically used in Artificial Intelligence, Computer Vision, Numpy applications. logsumexp has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Fast SSE logsumexp for python/numpy

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Quality

Security

License

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Support

logsumexp has a low active ecosystem.

It has 24 star(s) with 5 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of logsumexp is current.

Quality

logsumexp has 0 bugs and 0 code smells.

Security

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

logsumexp code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

logsumexp is licensed under the BSD-2-Clause License. This license is Permissive.

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

Reuse

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

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

It has 21 lines of code, 0 functions and 1 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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logsumexp Key Features

No Key Features are available at this moment for logsumexp.

logsumexp Examples and Code Snippets

No Code Snippets are available at this moment for logsumexp.

Community Discussions

Trending Discussions on logsumexp

Pytorch with CUDA throws RuntimeError when using pack_padded_sequence

Apply logsumexp to all the first element of array

Looking for faster way to implement logSumExp across multidimensional array

Java IntStream, Range and mapToDouble and reduce function equivalent in C#

Number of neurons for hidden layers

Fastest Python log-sum-exp in a 'reduceat'

QUESTION

Pytorch with CUDA throws RuntimeError when using pack_padded_sequence

Asked 2021-Jun-22 at 15:58

I am trying to train a BiLSTM-CRF on detecting new NER entities with Pytorch. To do so, I am using a snippet of code derivated from the Pytorch Advanced tutorial. This snippet implements batch training.

I followed the READ-ME in order to present data as required. Everything works great on CPU, but when I'm trying to get it to GPU, the following error occur :

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ANSWER

Answered 2021-Jun-22 at 15:58

Within PadSequence function (which acts as a collate_fn which gathers samples and makes a batch from them) you are explicitly casting to cuda device, namely:

Source https://stackoverflow.com/questions/68086528

QUESTION

Apply logsumexp to all the first element of array

Asked 2020-Sep-28 at 21:01

I have a question, under a specific variable that for semplicitity we call a, I have the following arrays written in this way.

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ANSWER

Answered 2020-Sep-28 at 21:01

Use the axis parameter: logsumexp(a, axis=?), where ? Is 0 or 1

Source https://stackoverflow.com/questions/64108876

QUESTION

Looking for faster way to implement logSumExp across multidimensional array

Asked 2020-Jul-17 at 19:42

I have a line in some R code I am writing that is quite slow. It applies logSumExp across a 4 dimensional array using the apply command. I'm wondering are there ways to speed it up!

Reprex: (this might take 10seconds or more to run)

...

ANSWER

Answered 2020-Jul-14 at 10:09

rowSums is a less general version of apply that is optimised for speed when adding up, so this can be used to speed up the calculation. Note the caveat in the helpfile ?rowSums if it's important to maintain a difference in your calculations between NA and NaN.

Source https://stackoverflow.com/questions/62891781

QUESTION

Java IntStream, Range and mapToDouble and reduce function equivalent in C#

Asked 2020-Jun-07 at 21:15

Can someone help me with what will the below lines of Java do ? Or can you give an C# equivalent of the below lines of code

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ANSWER

Answered 2020-Jun-07 at 18:09

Code using streams in Java usually translates well into LINQ in .NET.

map or mapToXXX works like Select, reduce is Aggregate, but here Sum is more convenient. IntStream.range is Enumerable.Range. Everything else should have a "obvious" equivalent.

Source https://stackoverflow.com/questions/62249224

QUESTION

Number of neurons for hidden layers

Asked 2020-Jun-04 at 23:09

Im trying to execute a Bayesian Neural Network that I found on the paper "Uncertainty on Deep Learning", Yarin Gal. I found this code on github:

...

ANSWER

Answered 2020-Jun-04 at 23:09

That syntax is correct vector = np.array([1, 2, 3]). That is the way to define a vector in python's numpy.

A neural network can have any number o hidden (internal) layers. And each layer will have a certain number of neurons.

So in this code, a vector=np.array([100, 150, 100]), means that the network should have 3 hidden layers (because the vector has 3 values), and the hidden layers should have, from input to output 100, 150, 100 neurons respectively.

Source https://stackoverflow.com/questions/62205486

QUESTION

Fastest Python log-sum-exp in a 'reduceat'

Asked 2020-Jan-22 at 18:42

As part of a statistical programming package, I need to add log-transformed values together with the LogSumExp Function. This is significantly less efficient than adding unlogged values together.

Furthermore, I need to add values together using the numpy.ufunc.reduecat functionality.

There are various options I've considered, with code below:

(for comparison in non-log-space) use numpy.add.reduceat
Numpy's ufunc for adding logged values together: np.logaddexp.reduceat
Handwritten reduceat function with the following logsumexp functions:
- scipy's implemention of logsumexp
- logsumexp function in Python (with numba)
- Streaming logsumexp function in Python (with numba)

...

ANSWER

Answered 2020-Jan-22 at 18:42

There is some room for improvement

But never expect logsumexp to be as fast as a standard summation, because exp is quite a expensive operation.

Example

Source https://stackoverflow.com/questions/59850770

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

Vulnerabilities

No vulnerabilities reported

Install logsumexp

You can download it from GitHub.

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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