Keras is a high-level neural networks open source API, written in Python and capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, MXNet, or Theano.
Keras has stronger adoption in both the industry and the research community than any other deep learning framework except TensorFlow (and Keras is commonly used together with TensorFlow).
Keras has been adopted by deep learning researchers, and researchers at large scientific organizations, in particular CERN and NASA.
Keras is compatible with Python 2.7-3.6.
Key Features
- Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
- Provides implementations of commonly used neural network building blocks such as layers, objectives, activation functions, optimizers.
- Supports both convolutional networks and recurrent networks, as well as combinations of the two. Convolutional networks currently set the state of the art in visual recognition.
- Code and pre-trained weights, which have been trained on the ImageNet database, are available for the following image classification models:
- Xception – a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions.
- VGG16 – Very Deep Convolutional Networks for Large-Scale Visual Recognition with 16 weight layers.
- VGG19 – Very Deep Convolutional Networks for Large-Scale Visual Recognition with 19 weight layers.
- ResNet50 – this network introduces residual learning. ResNet50 is a 50 layer Residual Network.
- Inception v3 – a variant of Inception-v2 which adds BN-auxiliary.
- Inception-ResNet v2 – a convolutional neural network (CNN) that achieves a new state of the art in terms of accuracy on the ILSVRC image classification benchmark.
- MobileNet v1 – a family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application.
- Runs seamlessly on CPU and GPU.
ImageNet is a common academic data set in machine learning for training an image recognition system.
Website: keras.io
Support: FAQ, GitHub code repository
Developer: François Chollet, Google, Microsoft, and many other contributors
License: MIT License
Keras is written in Python. Learn Python with our recommended free books and free tutorials.
Related Software
| Deep Learning with Python | |
|---|---|
| TensorFlow | A very popular Deep Learning framework |
| PyTorch | Tensors and Dynamic neural networks in Python |
| Keras | High-level neural networks API |
| fastai | Simplifies training fast and accurate neural networks |
| tinygrad | Compact, lightweight deep learning framework |
| PyTorch Lightning | Framework for scaling PyTorch training workflows |
| JAX | High-performance numerical computing and ML library |
| PyTensor | Library for fast numerical computation |
| MindSpore | Framework for training and deploying neural networks |
| Elephas | Distributed deep learning with Keras and Spark |
| Chainer | Powerful, flexible, and intuitive framework for neural networks |
| Equinox | Neural network library for building flexible models with JAX |
| Caffe | Convolutional Architecture for Fast Feature Embedding |
| TFlearn | Deep learning library featuring a higher-level API for TensorFlow |
| MXNet | Flexible and efficient library |
| CNTK | Distributed deep learning |
| Neupy | Python library for neural networks and deep learning |
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