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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

Test 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-searchCode 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 hadoopStacked 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.

Consider 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-programmingNNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning experiments. The tool dispatches and runs trial jobs that generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments (e.g. local machine, remote servers and cloud). This command will start an experiment and a WebUI. The WebUI endpoint will be shown in the output of this command (for example, http://localhost:8080). Open this URL in your browser. You can analyze your experiment through WebUI, or browse trials' tensorboard.

automl deep-learning neural-architecture-search hyperparameter-optimization optimizerkeras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind.

keras tensorflow theano reinforcement-learning neural-networks machine-learningThis library is the official extension repository for the python deep learning library Keras. It contains additional layers, activations, loss functions, optimizers, etc. which are not yet available within Keras itself. All of these additional modules can be used in conjunction with core Keras models and modules. As the community contributions in Keras-Contrib are tested, used, validated, and their utility proven, they may be integrated into the Keras core repository. In the interest of keeping Keras succinct, clean, and powerfully simple, only the most useful contributions make it into Keras. This contribution repository is both the proving ground for new functionality, and the archive for functionality that (while useful) may not fit well into the Keras paradigm.

keras theano tensorflow machine-learning deep-learning neural-networks data-scienceThis repository contains Keras/Tensorflow code for the "CRF-RNN" semantic image segmentation method, published in the ICCV 2015 paper Conditional Random Fields as Recurrent Neural Networks. This paper was initially described in an arXiv tech report. The online demo of this project won the Best Demo Prize at ICCV 2015. Original Caffe-based code of this project can be found here. Results produced with this Keras/Tensorflow code are almost identical to that with the Caffe-based version. The root directory of the clone will be referred to as crfasrnn_keras hereafter.

image-segmentation semantic-segmentation crf-as-rnn tensorflow keras crfasrnn crfasrnn-keras crfasrnn-tensorflow crf-rnn-tensorflow crf-rnn-kerasWe report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This generative model allows efficient search and optimization through open-ended spaces of chemical compounds. We train deep neural networks on hundreds of thousands of existing chemical structures to construct two coupled functions: an encoder and a decoder. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to the discrete representation from this latent space.

**This project is no longer active. Please check out TensorFlow.js.** The Keras.js demos still work but is no longer updated. Run Keras models in the browser, with GPU support provided by WebGL 2. Models can be run in Node.js as well, but only in CPU mode. Because Keras abstracts away a number of frameworks as backends, the models can be trained in any backend, including TensorFlow, CNTK, etc.

deep-learning machine-learning webgl tensorflow neural-networks keras deep learning neural networks webgl2 gpuJupyter notebooks for using & learning Keras

deep-learning keras-notebooks keraskeras-rcnn is the Keras package for region-based convolutional neural networks. The data is made up of a list of dictionaries corresponding to images.

deep-learning theano tensorflow cntk object-detection image-segmentation
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