RocAlphaGo | led replication of DeepMind 's 2016 Nature publication | Machine Learning library

by Rochester-NRT Python Version: Current License: MIT

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

kandi X-RAY | RocAlphaGo Summary

RocAlphaGo is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. RocAlphaGo has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However RocAlphaGo has 2 bugs. You can download it from GitHub.

(Previously known just as "AlphaGo," renamed to clarify that we are not affiliated with DeepMind). This project is a student-led replication/reference implementation of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search," details of which can be found on their website. This implementation uses Python and Keras - a decision to prioritize code clarity, at least in the early stages.

Support

Quality

Security

License

Reuse

Support

RocAlphaGo has a medium active ecosystem.

It has 9079 star(s) with 2491 fork(s). There are 943 watchers for this library.

It had no major release in the last 6 months.

There are 28 open issues and 96 have been closed. On average issues are closed in 130 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of RocAlphaGo is current.

Quality

RocAlphaGo has 2 bugs (0 blocker, 0 critical, 2 major, 0 minor) and 64 code smells.

Security

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

RocAlphaGo code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

RocAlphaGo 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

RocAlphaGo 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.

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RocAlphaGo Key Features

No Key Features are available at this moment for RocAlphaGo.

RocAlphaGo Examples and Code Snippets

No Code Snippets are available at this moment for RocAlphaGo.

Community Discussions

Trending Discussions on RocAlphaGo

keras: ValueError: Error when checking model target: expected activation_1 to have shape (None, 60) but got array with shape (10, 100)

Fastest Cython implementation depends on computer?

QUESTION

keras: ValueError: Error when checking model target: expected activation_1 to have shape (None, 60) but got array with shape (10, 100)

Asked 2018-Feb-19 at 11:24

I'm trying to transplant RocAlphaGo to play Game of Amazons, and there are problems when trying to implement supervised policy trainer.

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ANSWER

Answered 2018-Feb-19 at 11:24

As Matias is saying in the comments, if you add

Source https://stackoverflow.com/questions/48860388

QUESTION

Fastest Cython implementation depends on computer?

Asked 2017-Mar-21 at 15:17

I am converting a python script to cython and optimizing it for more speed. Right now i have 2 versions, on my desktop V2 is twice as fast as V1 unfortunately on my laptop V1 is twice as fast as V2 and i am unable to find out why there is such a big difference. Both computers use:
- Ubuntu 16.04
- Python 2.7.12
- Cython 0.25.2
- Numpy 1.12.1
Desktop:
- Intel® Core™ i3-4370 CPU @ 3.80GHz × 4 64bit. 16GB RAM
Laptop:
- Intel® Core™ i5-3210 CPU @ 2.5GHz × 2 64bit. 8GB RAM

V1 - you can find the full code here. the only changes made are renaming go.py, preprocessing.py to go.pyx, preprocessing.pyx and using
import pyximport; pyximport.install() to compile them. you can run test.py. This version is using a 2d numpy array board to store data in go.pyx and list comprehension in the get_board function in preprocessing.pyx to process data. during the test no function is called from go.py only the numpy array board is used

V2 - you can find the full code here. quite some stuff has changed, below you can find a list with everything affecting this test case. Be aware, all function and variable declarations have to be in go.pxd. you can run test.py using this command: python test.py build_ext --inplace
the 2d numpy array is replaced by:

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ANSWER

Answered 2017-Mar-20 at 21:07

They are two different machines and behave differently. There's a reason why processor reviews use large benchmark suites. It could be said that the desktop CPU performs better on average, but execution times between two small but non-trivial pieces of codes does not 'have' to favor the desktop CPU. And differences execution times definitely do not have to follow any linear relationship. The performance is always dependant on a huge amount of factors. Possible explanations include but are not limited to the smaller L1 and L2 caches on the desktop and the change in vector instruction sets from AVX to AVX2 between the Ivy Bridge laptop and the Haswell desktop.

Generally it's a good idea to concentrate on using good algorithms and to identify and remove bottlenecks when optimizing performance. Trying to stare at benchmarks between different machines will probably only cause a headache.

Source https://stackoverflow.com/questions/42911598

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

Vulnerabilities

No vulnerabilities reported

Install RocAlphaGo

You can download it from GitHub.
You can use RocAlphaGo 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.