Keras

Keras: Deep Learning library for Theano and TensorFlow. Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Use Keras if you need a deep learning library that: allows for easy and fast prototyping (through total modularity, minimalism, and extensibility). supports both convolutional networks and recurrent networks, as well as combinations of the two. supports arbitrary connectivity schemes (including multi-input and multi-output training). runs seamlessly on CPU and GPU. Read the documentation at Keras.io. Keras is compatible with: Python 2.7-3.5.


References in zbMATH (referenced in 211 articles )

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  1. Atashgahi, Zahra; Sokar, Ghada; van der Lee, Tim; Mocanu, Elena; Mocanu, Decebal Constantin; Veldhuis, Raymond; Pechenizkiy, Mykola: Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders (2022)
  2. Chandna, Akshat; Srinivasan, Sanjay: Mapping natural fracture networks using geomechanical inferences from machine learning approaches (2022)
  3. Chawshin, Kurdistan; Berg, Carl Fredrik; Varagnolo, Damiano; Lopez, Olivier: Automated porosity estimation using CT-scans of extracted core data (2022)
  4. Dash, Tirtharaj; Srinivasan, Ashwin; Baskar, A.: Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment (2022)
  5. des Mesnards, Nicolas Guenon; Hunter, David Scott; El Hjouji, Zakaria; Zaman, Tauhid: Detecting bots and assessing their impact in social networks (2022)
  6. Dubois, Pierre; Gomez, Thomas; Planckaert, Laurent; Perret, Laurent: Machine learning for fluid flow reconstruction from limited measurements (2022)
  7. Eichinger, Matthias; Heinlein, Alexander; Klawonn, Axel: Surrogate convolutional neural network models for steady computational fluid dynamics simulations (2022)
  8. Frankel, Ari; Hamel, Craig M.; Bolintineanu, Dan; Long, Kevin; Kramer, Sharlotte: Machine learning constitutive models of elastomeric foams (2022)
  9. Hertel, Lars; Baldi, Pierre; Gillen, Daniel L.: Reproducible hyperparameter optimization (2022)
  10. Hoang, Chi; Chowdhary, Kenny; Lee, Kookjin; Ray, Jaideep: Projection-based model reduction of dynamical systems using space-time subspace and machine learning (2022)
  11. Jiazhen Gu, Xuchuan Luo, Yangfan Zhou, Xin Wang: Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing (2022) arXiv
  12. Knoblauch, Andreas: On the antiderivatives of (x^p/(1 - x)) with an application to optimize loss functions for classification with neural networks (2022)
  13. Kuczyński, M. D.; Borchardt, M.; Kleiber, R.; Könies, A.; Nührenberg, C.: Magnetohydrodynamic eigenfunction classification with a Neural Network (2022)
  14. Larios-Cárdenas, Luis Ángel; Gibou, Frédéric: A hybrid inference system for improved curvature estimation in the level-set method using machine learning (2022)
  15. Paul Scherer, Thomas Gaudelet, Alison Pouplin, Suraj M S, Jyothish Soman, Lindsay Edwards, Jake P. Taylor-King: PyRelationAL: A Library for Active Learning Research and Development (2022) arXiv
  16. Petrasova, Iveta; Karban, Pavel; Kropik, Petr; Panek, David; Dolezel, Ivo: Optimization of selected operation characteristics of array antennas (2022)
  17. Pfannschmidt, Karlson; Gupta, Pritha; Haddenhorst, Björn; Hüllermeier, Eyke: Learning context-dependent choice functions (2022)
  18. Razak, Syamil Mohd; Jiang, Anyue; Jafarpour, Behnam: Latent-space inversion (LSI): a deep learning framework for inverse mapping of subsurface flow data (2022)
  19. Reiners, Malena; Klamroth, Kathrin; Heldmann, Fabian; Stiglmayr, Michael: Efficient and sparse neural networks by pruning weights in a multiobjective learning approach (2022)
  20. Ribeiro, Eugénio; Ribeiro, Ricardo; Martins de Matos, David: Automatic recognition of the general-purpose communicative functions defined by the ISO 24617-2 standard for dialog act annotation (2022)

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