pytensor | A numpy deep learning framework | Machine Learning library

by xinjli Python Version: Current License: MIT

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kandi X-RAY | pytensor Summary

pytensor is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Numpy, Neural Network applications. pytensor 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.

pytensor is a deep learning framework implemented with pure numpy.

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pytensor has a low active ecosystem.

It has 16 star(s) with 4 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

pytensor has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pytensor is current.

Quality

pytensor has no bugs reported.

Security

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

License

pytensor is licensed under the MIT License. This license is Permissive.

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

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pytensor 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, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Compute the numerical gradient of the given tensors
Clear gradients
Clear forward ops
Convert input to list of words
Get the ids of the words
Get the ID for a word
Get the embeddings for the given name and shape
Get a tensor
Forward computation
Register the graph
Train the model
Evaluate the model
Compute the output tensor
Backward operations
Compute the input tensors
Reset gradients
Train MLP dataset
Calculates the loss of the target tensor
Calculate the mean squared loss of the input tensor
Compute the affine transformation
Train the LSTML model
Perform forward computation
Returns the output tensor
Train the RNN model
Compute the embedding tensor
Create a new output tensor

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pytensor Key Features

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pytensor Examples and Code Snippets

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Community Discussions

Trending Discussions on pytensor

Performance of xtensor types vs. NumPy for simple reduction

QUESTION

Performance of xtensor types vs. NumPy for simple reduction

Asked 2017-Nov-23 at 10:55

I was trying out xtensor-python and started by writing a very simple sum function, after using the cookiecutter setup and enabling SIMD intrinsics with xsimd.

...

ANSWER

Answered 2017-Nov-23 at 10:55

wow this is a coincidence! I am working on exactly this speedup!

xtensor's sum is a lazy operation -- and it doesn't use the most performant iteration order for (auto-)vectorization. However, we just added a evaluation_strategy parameter to reductions (and the upcoming accumulations) which allows you to select between immediate and lazy reductions.

Immediate reductions perform the reduction immediately (and not lazy) and can use a iteration order optimized for vectorized reductions.

You can find this feature in this PR: https://github.com/QuantStack/xtensor/pull/550

In my benchmarks this should be at least as fast or faster than numpy. I hope to get it merged today.

Btw. please don't hesitate to drop by our gitter channel and post a link to the question, we need to monitor StackOverflow better: https://gitter.im/QuantStack/Lobby

Source https://stackoverflow.com/questions/47240338

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

Vulnerabilities

No vulnerabilities reported

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