https:///why-use-keras/There are countless deep learning frameworks available today. Why use Keras rather than any other? Here are some of the areas in which Keras compares favorably to existing alternatives. 今天有无数的深度学习框架。 为什么要使用Keras而不是其他的？ 以下是凯拉斯与现有替代品相比的一些区域。 Keras prioritizes developer experienceKeras优先考虑开发人员的经验
Keras has broad adoption in the industry and the research community。Keras在业界和研究界广泛采用 With over 200,000 individual users as of November 2017, Keras has stronger adoption in both the industry and the research community than any other deep learning framework except TensorFlow itself (and Keras is commonly used in conjunction with TensorFlow). 截至2017年11月，截至2017年11月，Keras拥有超过20万的个人用户，除了TensorFlow本身（和Keras通常与TensorFlow结合使用）之外，Keras在行业和研究团体中的应用都强于其他任何深度学习框架。 You are already constantly interacting with features built with Keras -- it is in use at Netflix, Uber, Yelp, Instacart, Zocdoc, Square, and many others. It is especially popular among startups that place deep learning at the core of their products. 您已经在不断与Keras构建的功能进行交互 - 它正在Netflix，Uber，Yelp，Instacart，Zocdoc，Square等等使用。 它在深度学习成为其核心产品的创业公司中尤其受到欢迎。 Keras is also a favorite among deep learning researchers, coming in #2 in terms of mentions in scientific papers uploaded to the preprint server arXiv.org: Keras也是深度学习研究人员的最爱，在上载到预印本服务器arXiv.org的科学论文中提及＃2：
Keras has also been adopted by researchers at large scientific organizations, in particular CERN and NASA. Keras也被大型科学组织的研究人员采用，特别是CERN和NASA。 Keras makes it easy to turn models into productsKeras可以轻松将模型转化为产品 Your Keras models can be easily deployed across a greater range of platforms than any other deep learning framework: 与任何其他深度学习框架相比，您的Keras模型可以轻松部署在更广泛的平台上：
Keras supports multiple backend engines and does not lock you into one ecosystemKeras支持多个后端引擎，并且不会将您锁定到一个生态系统中 Your Keras models can be developed with a range of different deep learning backends. Importantly, any Keras model that only leverages built-in layers will be portable across all these backends: you can train a model with one backend, and load it with another (e.g. for deployment). Available backends include: 您的Keras模型可以开发一系列不同的深度学习后端。 重要的是，任何只利用内置图层的Keras模型都可以在所有这些后端中移植：您可以训练带有一个后端的模型，并使用另一个后端（例如用于部署）加载模型。 可用的后端包括：
Amazon is also currently working on developing a MXNet backend for Keras. 亚马逊目前也在为Keras开发一个MXNet后端。 As such, your Keras model can be trained on a number of different hardware platforms beyond CPUs: 因此，您的Keras模型可以在CPU之外的多种不同硬件平台上进行训练：
Keras has strong multi-GPU support and distributed training supportKeras拥有强大的多GPU支持和分布式训练支持
Keras development is backed by key companies in the deep learning ecosystem凯拉斯的发展得到深度学习生态系统中的关键公司的支持 Keras development is backed primarily by Google, and the Keras API comes packaged in TensorFlow as Keras开发主要由Google支持，Keras API以Tf.keras的形式包装在TensorFlow中。 此外，Microsoft维护了CNTK Keras后端。 亚马逊AWS正在开发MXNet支持。 其他贡献公司包括NVIDIA，Uber和Apple（与CoreML）。
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