- 732

A very simple convenience wrapper around hyperopt for fast prototyping with keras models. Hyperas lets you use the power of hyperopt without having to learn the syntax of it. Instead, just define your keras model as you are used to, but use a simple template notation to define hyper-parameter ranges to tune. To do hyper-parameter optimization on this model, just wrap the parameters you want to optimize into double curly brackets and choose a distribution over which to run the algorithm.

http://maxpumperla.github.io/hyperas/https://github.com/maxpumperla/hyperas

Tags | hyperopt keras hyperparameter-optimization |

Implementation | Python |

License | MIT |

Platform | Windows Linux |

Hyperopt-sklearn is Hyperopt-based model selection among machine learning algorithms in scikit-learn. If you are familiar with sklearn, adding the hyperparameter search with hyperopt-sklearn is only a one line change from the standard pipeline.

Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.

Distributed Asynchronous Hyperparameter Optimization in Python

KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search algorithms. KerasTuner requires Python 3.6+ and TensorFlow 2.0+.

This code provides a hyper-parameter optimization implementation for machine learning algorithms, as described in the paper: L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, 2020, doi: https://doi.org/10.1016/j.neucom.2020.07.061. To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model's performance. In this paper, optimizing the hyper-parameters of common machine learning models is studied. We introduce several state-of-the-art optimization techniques and discuss how to apply them to machine learning algorithms. Many available libraries and frameworks developed for hyper-parameter optimization problems are provided, and some open challenges of hyper-parameter optimization research are also discussed in this paper. Moreover, experiments are conducted on benchmark datasets to compare the performance of different optimization methods and provide practical examples of hyper-parameter optimization.

machine-learning deep-learning random-forest optimization svm genetic-algorithm machine-learning-algorithms hyperparameter-optimization artificial-neural-networks grid-search tuning-parameters knn bayesian-optimization hyperparameter-tuning random-search particle-swarm-optimization hpo python-examples python-samples hyperbandTest tube is a python library to track and parallelize hyperparameter search for Deep Learning and ML experiments. It's framework agnostic and built on top of the python argparse API for ease of use. If you're a researcher, test-tube is highly encouraged as a way to post your paper's training logs to help add transparency and show others what you've tried that didn't work.

deep-learning machine-learning tensorflow hyperparameter-optimization neural-networks data-science keras pytorch caffe2 caffe chainer grid-search random-searchOptuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters. Please refer to sample code below. The goal of a study is to find out the optimal set of hyperparameter values (e.g., classifier and svm_c) through multiple trials (e.g., n_trials=100). Optuna is a framework designed for the automation and the acceleration of the optimization studies.

machine-learning parallel distributed hyperparameter-optimization hacktoberfestRL Baselines3 Zoo is a training framework for Reinforcement Learning (RL), using Stable Baselines3. It provides scripts for training, evaluating agents, tuning hyperparameters, plotting results and recording videos.

reinforcement-learning robotics optimization lab openai gym hyperparameter-optimization rl sde hyperparameter-tuning hyperparameter-search pybullet stable-baselines pybullet-environments tuning-hyperparametersCode for tuning hyperparams with Hyperband, adapted from Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. Use defs.meta/defs_regression.meta to try many models in one Hyperband run. This is an automatic alternative to constructing search spaces with multiple models (like defs.rf_xt, or defs.polylearn_fm_pn) by hand.

hyperparameters hyperparameter-optimization hyperparameter-tuning gradient-boosting-classifier gradient-boosting machine-learningauto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

automl scikit-learn automated-machine-learning hyperparameter-optimization hyperparameter-tuning hyperparameter-search bayesian-optimization metalearning meta-learning smacAttention: This package is under heavy development and subject to change. A stable release of SMAC (v2) in Java can be found here. The documentation can be found here.

bayesian-optimization bayesian-optimisation hyperparameter-optimization hyperparameter-tuning hyperparameter-search configuration algorithm-configuration automl automated-machine-learningauto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. All of these projects are ready for production. These projects all have prediction time in the 1 millisecond range for a single prediction, and are able to be serialized to disk and loaded into a new environment after training.

machine-learning data-science automated-machine-learning gradient-boosting scikit-learn machine-learning-pipelines machine-learning-library production-ready automl lightgbm analytics feature-engineering hyperparameter-optimization deep-learning xgboost keras deeplearning tensorflow artificial-intelligenceDistributed Deep Learning with Apache Spark and Keras. Distributed Keras is a distributed deep learning framework built op top of Apache Spark and Keras, with a focus on "state-of-the-art" distributed optimization algorithms. We designed the framework in such a way that a new distributed optimizer could be implemented with ease, thus enabling a person to focus on research. Several distributed methods are supported, such as, but not restricted to, the training of ensembles and models using data parallel methods.

machine-learning deep-learning apache-spark data-parallelism distributed-optimizers keras optimization-algorithms tensorflow data-science hadoopAdaTune is a library to perform gradient based hyperparameter tuning for training deep neural networks. AdaTune currently supports tuning of the learning_rate parameter but some of the methods implemented here can be extended to other hyperparameters like momentum or weight_decay etc. AdaTune provides the following gradient based hyperparameter tuning algorithms - HD, RTHO and our newly proposed algorithm, MARTHE. The repository also contains other commonly used non-adaptive learning_rate adaptation strategies like staircase-decay, exponential-decay and cosine-annealing-with-restarts. The library is implemented in PyTorch. The goal of the methods in this package is to automatically compute in an online fashion a learning rate schedule for stochastic optimization methods (such as SGD) only on the basis of the given learning task, aiming at producing models with associated small validation error.

machine-learning deep-learning pytorch neural-networks hyperparameter-tuning automl learning-rate-schedulingThe TensorFlow Model Optimization Toolkit is a suite of tools that users, both novice and advanced, can use to optimize machine learning models for deployment and execution. Supported techniques include quantization and pruning for sparse weights. There are APIs built specifically for Keras.

machine-learning sparsity compression deep-learning tensorflow optimization keras ml pruning quantization model-compression quantized-training quantized-neural-networks quantized-networksDetermined integrates these features into an easy-to-use, high-performance deep learning environment — which means you can spend your time building models instead of managing infrastructure. To use Determined, you can continue using popular DL frameworks such as TensorFlow and PyTorch; you just need to update your model code to integrate with the Determined API.

kubernetes machine-learning deep-learning tensorflow pytorch hyperparameter-optimization hyperparameter-tuning hyperparameter-search distributed-training ml-infrastructure ml-platformStacked ensembles are simple in theory. You combine the predictions of smaller models and feed those into another model. However, in practice, implementing them can be a major headache. Xcessiv holds your hand through all the implementation details of creating and optimizing stacked ensembles so you're free to fully define only the things you care about.

machine-learning ensemble-learning stacked-ensembles scikit-learn data-science hyperparameter-optimization automated-machine-learningA Keras model that addresses the Quora Question Pairs [1] dyadic prediction task. The model architecture is based on the Stanford Natural Language Inference [2] benchmark model developed by Stephen Merity [3], specifically the version using a simple summation of GloVe word embeddings [4] to represent each question in the pair. A difference between this and the Merity SNLI benchmark is that our final layer is Dense with sigmoid activation, as opposed to softmax. Another key difference is that we are using the max operator as opposed to sum to combine word embeddings into a question representation. We use binary cross-entropy as a loss function and Adam for optimization.

Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production models. If you have any questions, please don't hesitate to ask in our Slack community.

machine-learning reinforcement-learning deep-learning tensorflow keras pytorch hyperparameter-search experiment-trackConsider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.

machine-learning data-science automl automation scikit-learn hyperparameter-optimization model-selection parameter-tuning automated-machine-learning random-forest gradient-boosting feature-engineering xgboost genetic-programming
We have large collection of open source products. Follow the tags from
Tag Cloud >>

Open source products are scattered around the web. Please provide information
about the open source projects you own / you use.
**Add Projects.**