keras 简介

Keras-简介 (Keras - Introduction)

Deep learning is one of the major subfield of machine learning framework. Machine learning is the study of design of algorithms, inspired from the model of human brain. Deep learning is becoming more popular in data science fields like robotics, artificial intelligence(AI), audio & video recognition and image recognition. Artificial neural network is the core of deep learning methodologies. Deep learning is supported by various libraries such as Theano, TensorFlow, Caffe, Mxnet etc., Keras is one of the most powerful and easy to use python library, which is built on top of popular deep learning libraries like TensorFlow, Theano, etc., for creating deep learning models.

深度学习是机器学习框架的主要子领域之一。 机器学习是受人脑模型启发而进行的算法设计研究。 深度学习在机器人，人工智能(AI)，音频和视频识别以及图像识别等数据科学领域正变得越来越流行。 人工神经网络是深度学习方法的核心。 深度学习受到Theano，TensorFlow，Caffe，Mxnet等各种库的支持，Keras是功能最强大且易于使用的python库之一，它基于流行的深度学习库如TensorFlow，Theano等构建。 ，用于创建深度学习模型。

Keras概述 (Overview of Keras)

Keras runs on top of open source machine libraries like TensorFlow, Theano or Cognitive Toolkit (CNTK). Theano is a python library used for fast numerical computation tasks. TensorFlow is the most famous symbolic math library used for creating neural networks and deep learning models. TensorFlow is very flexible and the primary benefit is distributed computing. CNTK is deep learning framework developed by Microsoft. It uses libraries such as Python, C#, C++ or standalone machine learning toolkits. Theano and TensorFlow are very powerful libraries but difficult to understand for creating neural networks.

Keras在TensorFlow，Theano或Cognitive Toolkit(CNTK)等开源机器库之上运行。 Theano是用于快速数值计算任务的python库。 TensorFlow是用于创建神经网络和深度学习模型的最著名的符号数学库。 TensorFlow非常灵活，主要优势是分布式计算。 CNTK是Microsoft开发的深度学习框架。 它使用Python，C＃，C ++或独立的机器学习工具包之类的库。 Theano和TensorFlow是非常强大的库，但是对于创建神经网络却很难理解。

Keras is based on minimal structure that provides a clean and easy way to create deep learning models based on TensorFlow or Theano. Keras is designed to quickly define deep learning models. Well, Keras is an optimal choice for deep learning applications.

Keras基于最小的结构，它提供了一种干净，简便的方法来创建基于TensorFlow或Theano的深度学习模型。 Keras旨在快速定义深度学习模型。 好吧，Keras是深度学习应用程序的最佳选择。

特征 (Features)

Keras leverages various optimization techniques to make high level neural network API easier and more performant. It supports the following features −

Keras利用各种优化技术来简化高级神经网络API，并提高其性能。 它支持以下功能-

• Consistent, simple and extensible API.

  一致，简单和可扩展的API。

• Minimal structure - easy to achieve the result without any frills.

  最小的结构-容易获得结果，没有任何多余的装饰。

• It supports multiple platforms and backends.

  它支持多个平台和后端。

• It is user friendly framework which runs on both CPU and GPU.

  它是用户友好的框架，可在CPU和GPU上运行。

• Highly scalability of computation.

  计算的高度可扩展性。

好处 (Benefits)

Keras is highly powerful and dynamic framework and comes up with the following advantages −

Keras是一个功能强大且动态的框架，具有以下优点-

• Larger community support.

  更大的社区支持。

• Easy to test.

  易于测试。

• Keras neural networks are written in Python which makes things simpler.

  Keras神经网络是用Python编写的，这使事情变得更简单。

• Keras supports both convolution and recurrent networks.

  Keras支持卷积和循环网络。

• Deep learning models are discrete components, so that, you can combine into many ways.

  深度学习模型是离散的组件，因此，您可以将其组合成多种方式。

翻译自: https://www.tutorialspoint.com/keras/keras_introduction.htm

keras 简介