Skip to content
Sections
>> Trisquel >> Paquets >> nabia >> python >> python3-keras-preprocessing
nabia  ] [  aramo  ]
[ Paquet source : keras-preprocessing  ]

Paquet : python3-keras-preprocessing (1.0.5-1)

data preprocessing module for the Keras deep learning framework

Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation.

Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing).

It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian).

Keras Preprocessing is the data preprocessing and data augmentation module of the Keras deep learning library. It provides utilities for working with image data, text data, and sequence data.

Autres paquets associés à python3-keras-preprocessing

  • dépendances
  • recommandations
  • suggestions
  • dep: python3
    interactive high-level object-oriented language (default python3 version)
  • dep: python3-numpy
    Fast array facility to the Python 3 language
  • dep: python3-six
    Python 2 and 3 compatibility library (Python 3 interface)
  • rec: python3-keras
    deep learning framework running on Theano or TensorFlow

Télécharger python3-keras-preprocessing

Télécharger pour toutes les architectures proposées
Architecture Taille du paquet Espace occupé une fois installé Fichiers
all 27,8 ko152 ko [liste des fichiers]