mccabe
tensorflow
mccabe | tensorflow | |
---|---|---|
5 | 223 | |
625 | 182,575 | |
0.0% | 0.5% | |
2.1 | 10.0 | |
5 months ago | 6 days ago | |
Python | C++ | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mccabe
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Python toolkits
mccabe for Ned’s script to check McCabe complexity
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Why do people use multiple scripts when programming in Python?
Cyclomatic Complexity is a metric used to determine the stability of your code. It basically boils down to the more code you have the more problems that can arise in said code. There are even modules for python to check your cyclomatic complexity. It goes hand in hand with separating your code out into modules. I work for a FAANG company and we usually want to keep our cyclomatic complexity less than 10 with that tool above.
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How to Audit the Quality of Your Python Code: A Step-by-Step Guide (Checklist Inside)
Mccabe—a Python complexity checker;
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Pybudget: A Solution to My Small-Brain Financial Decisions
A more advanced best practice would be separating different functions of your code into different files to keep Cyclomatic Complexity low. More code usually = more problems can be in said code. There’s even a tool you can use to determine how complex your code is called mccabe. Lower is better with that
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Code Quality Tools in Python
Flake8: a combination of following linters: PyFlakes, pycodestyle, Ned Batchelder’s McCabe script
tensorflow
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Side Quest Devblog #1: These Fakes are getting Deep
# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")
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Google lays off its Python team
[3]: https://github.com/tensorflow/tensorflow/graphs/contributors
- TensorFlow-metal on Apple Mac is junk for training
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐
- One .gitignore to rule them all
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10 Github repositories to achieve Python mastery
Explore here.
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GitHub and Developer Ecosystem Control
Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.
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Non-determinism in GPT-4 is caused by Sparse MoE
Right but that's not an inherent GPU determinism issue. It's a software issue.
https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.
Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.
Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.
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Can someone explain how keras code gets into the Tensorflow package?
and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:
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Is it even possible to design a ML model without using Python or MATLAB? Like using C++, C or Java?
Exactly what language do you think TensorFlow is written in? :)
What are some alternatives?
pylama - Code audit tool for python.
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
flake8-length - Flake8 plugin for a smart line length validation.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
autopep8 - A tool that automatically formats Python code to conform to the PEP 8 style guide.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
pyflakes - A simple program which checks Python source files for errors
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
isort - A Python utility / library to sort imports.
scikit-learn - scikit-learn: machine learning in Python
pybudget - This is a python script that will determine a budget for your current pay period.
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.