keras-tuner
nni
| keras-tuner | nni | |
|---|---|---|
| 4 | 5 | |
| 2,827 | 13,765 | |
| 0.6% | 0.6% | |
| 7.8 | 6.7 | |
| about 1 month ago | 2 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
keras-tuner
- Is there any premade evolutionary algorithm selecting optimal NN architectures in TensorFlow ?
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What has priority in the performance?
If you are using tensorflow, you can do all of that with the very elegant (in my opinion) https://keras.io/keras_tuner/
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Space Science with Python - Asteroids meet Deep Learning #10
today I'd like to show you how to optimize a Conv1D network using Keras-Tuner. It enables one to automatically test some pre-defined networks; or it applies Bayesian or Hyperband optimization to find the best model!
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How to know how many layers (LSTM and dense) should I create and how to know the right parameters? (beginner)
Tbh, just try and error. There is no right or wrong. You can use the Hyperparameter Tuner from keras to define some architectures with varying number of layers and units as well as some other hyperparameters.
nni
- Filter Pruning for PyTorch
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Automated Machine Learning (AutoML) - 9 Different Ways with Microsoft AI
For a complete tutorial, navigate to this Jupyter Notebook: https://github.com/microsoft/nni/blob/master/examples/notebooks/tabular_data_classification_in_AML.ipynb
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[D] Efficient ways of choosing number of layers/neurons in a neural network
optuna, hyperopt, nni, plenty of less-known tools too.
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Top 10 Developer Trends, Sun Oct 18 2020
microsoft / nni
What are some alternatives?
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
optuna - A hyperparameter optimization framework
deephyper - DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
vizier - Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
autogluon - Fast and Accurate ML in 3 Lines of Code
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
AutoML - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/Cream]
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
archai - Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
automlbenchmark - OpenML AutoML Benchmarking Framework
tsflex - Flexible time series feature extraction & processing