Keras is a Python-based deep learning library, unlike other deep learning frameworks. Keras acts as a high-level API specification for neural networks. It can serve as both a user interface and the functionality of other deep learning framework backends in which it runs.
Keras was initially a simplistic front-end to the popular Theano framework in academia. Since then, the Keras API has become part of Google TensorFlow. Keras officially supports Microsoft Cognitive Toolkit (CNTK), Deeplearning4J, and will soon support Apache MXNet.
Given its broad support, Keras’ position as an inter-framework migration tool cannot be shaken. Developers can not only transplant deep learning neural network algorithms and models, but also transplant pre-trained networks and weights.
About Keras
The origin of the Keras name Chollet created Keras as an API for the neural network of the Open Neural Electronic Robotic Operating System (ONEIROS) robotic research project. The name ONEIROS is a tribute to the ancient Greek epic Odyssey. In this epic, the mythical figure Oneiroi (the singular form of Oneiros) points out to humans two paths into the dream: a road through the magnificent ivory gate Into the nightmare, the other way through the low door of the horns of the beast, and ultimately presents a sacred scene. Keras means horns in Greek, which is a good name because the Keras API is designed to provide a shortcut for use with neural networks.
Keras is an open source Python package licensed under the auspices of the Massachusetts Institute of Technology (MIT) and partially owned by François Chollet, Google, Microsoft, and other contributors.
The Keras front-end supports the rapid construction of prototypes of neural network models in research. This API is easy to learn and use, and has the added advantage of easily migrating models from one framework to another.
Due to the independence of Keras, it does not need to interact with the back-end framework that runs it. Keras has its own graphical data structure that defines the computational graph: it does not rely on the underlying data structure of the backend framework. This approach frees you from learning to program the backend framework, and as such, Google has chosen to add the Keras API to its TensorFlow core.
This article will provide an overview of Keras, including the advantages of this framework, supported platforms, installation considerations, and supported backends.
The Keras Advantage
Why use Keras? It has many advantages, including:
A better deep learning application user experience (UX). The Keras API is user-friendly. This API is well-designed, object-oriented and flexible and easy to use, thus improving the user experience. Researchers can define new deep learning models without the use of potentially complex backends, resulting in more concise code.
Seamless Python integration. Keras is a native Python package that provides easy access to the entire Python data science ecosystem. For example, the Python Scikit-learn API can also use the Keras model. Developers familiar with the backend (such as TensorFlow) can also use Python to extend Keras.
Large portable work subjects and a strong knowledge base. Currently, researchers have used Keras with the Theano back-end for some time. This resulted in a large workforce and a strong community knowledge base that can be easily transferred from the Theano backend to the TensorFlow backend for deep learning developers. You can even migrate weights between backends, which means that pre-trained models can easily switch backends with just a few adjustments. The Keras and Theano studies are still closely related to TensorFlow and other backends. In addition, Keras also offers many free learning resources, documentation, and code samples.
Keras application
With Keras capabilities such as fitting generators, data pre-processing, and real-time data augmentation, developers can train powerful image classifiers with just a few training data sets. Keras comes with pre-trained built-in image classifier models including: Inception-ResNet-v2, Inception-v3, MobileNet, ResNet-50, VGG16, VGG19, and Xception.
Note: Because the sources of these models are different, there are several different licenses used to control the weight usage of these models.
With Keras, complex models can be defined in just a few lines of code. Keras is particularly suitable for training convolutional neural networks with small training data sets. Although Keras has gained wider use in image classification applications, it also applies to natural language processing (NLP) applications for text and speech.
Which platforms support Keras?
Platforms that support the Python development environment also support Keras. Formally built tests are run on Python V2.7x and V3.5, but the back end used with Keras requires a specific platform to access the supported graphics processing unit (GPU). Most backends depend on other software, such as the NVIDIA® CUDA® Toolkit and the CUDA Deep Neural Network Library (cuDNN).
TensorFlow is Keras's default backend, but it also supports Theano and CNTK backends. Support for Apache MXNet is still in progress, and Keras also provides an R interface. Many vendors have already ported the Keras API to their deep learning products, which allows them to import Keras models. For example, Java-based backend Eclipse Deeplearning4j can import Keras models. In addition, Scala wrappers are also available for Keras. Therefore, Keras platform support has become a controversial point. More importantly, make sure that the target platform supports your chosen Keras backend.
For more information on which platforms support TensorFlow, see Getting Started with TensorFlow. For more information on which platforms support Theano, read the Theano documentation. For more information on which platforms support CNTK, see the CNTK documentation.
Optional dependencies
Keras manages data by using the open source Hierarchical Data Format 5 (HDF5) binary format. Therefore, it needs to use HDF5 and its h5py Python wrapper to save the Keras model to disk. Keras draws graphics using the open source GraphViz DOT format. Therefore, it requires the use of GraphViz and its pydot Python wrapper to visualize the data. Keras GPU support also requires the use of the cuDNN library.
Build Keras from source code
Since Keras is a pure Python package, there is no reason to build from source code. Keras does not include any platform-specific backend code. It is highly recommended to install Keras from the Python Package Index (PyPI) instead.
Keras Installation Notes
As mentioned above, Keras can run on any platform that supports the Python development environment. This is enough to train and test most of the simple examples and tutorials. Most experts strongly recommend using high-performance computing (HPC) platforms for applications such as research or business development.
Because Keras uses a third-party backend, there are no installation considerations. The backend will be responsible for performing hardware acceleration. In summary, developers who install Keras backend should consider the following factors and options:
Processor and memory requirements
Virtual Machine Options
Docker installation options
Cloud installation options
Processor and memory requirements
Deep learning algorithms are computationally intensive, requiring at least one fast multicore CPU with vector expansion. In addition, one or more GPU cards that support high-end CUDA are standard in deep learning environments.
Deep learning processes communicate with each other using buffers in shared memory. Therefore, the allocated memory should be sufficient. Most experts therefore also recommend the use of larger CPU and GPU RAM because memory transfer is very expensive from the perspective of performance and energy use. Larger RAM avoids these operations.
Virtual Machine Options
The virtual machine (VM) for deep learning is currently best suited for many core CPU-centric hardware. Because the host operating system controls the physical GPU, implementing GPU acceleration on the VM is complicated. There are two main methods:
GPU pass-through:
Only applies to Type 1 hypervisors, such as Citrix Xen, VMware ESXi, Kernel Virtual Machine, and IBM® Power®.
Depending on the specific combination of CPU, chipset, hypervisor, and operating system, the overhead of the pass-through method may vary. Typically, the overhead is much lower for the latest generation of hardware.
A given hypervisor-operating system combination only supports specific NVIDIA GPU cards.
GPU virtualization:
Supported by major GPU vendors, including NVIDIA GRIDTM, AMD MxGPU and Intel® Graphics Virtualization Technology.
The latest release supports Open Computing Language (OpenCL) on specific newer GPUs. On most major backends (including TensorFlow), there is no formal OpenCL support.
The latest version of NVIDIA GRID can support CUDA and OpenCL on specific newer GPUs.
Docker installation options
Running Keras in a Docker container or Kubernetes cluster has many advantages. The Keras repository contains a Docker file with CUDA support for Mac OS X and Ubuntu. This image supports Theano or TensorFlow backends. The main advantage of using Docker is that the back end can access and run the physical GPU core (device).
Cloud installation options
There are many options for running Keras on cloud services. Keras can be used to train models on a supplier ecosystem, but with minor adjustments can be used for production deployment on another supplier ecosystem.
IBM Cloud® data science and data management provides Jupyter Notebook and Spark for Python environments. Keras and TensorFlow are pre-installed. The Kubernetes cluster on the IBM Cloud can run Keras and TensorFlow Docker images.
Google Cloud: Google offers machine instances that can access 1, 4 or 8 NVIDIA GPU devices in specific regions. Keras and TensorFlow can also be run on containerized GPU-backed Jupyter Notebooks
Amazon Web Services: Amazon offers the Amazon Web Services Deep Learning Amazon Machine Image (AMI) and optional NVIDIA GPU support that runs on a variety of Amazon Elastic Compute Cloud instances. Keras, TensorFlow, and other deep learning frameworks are pre-installed. The AMI can support up to 64 CPU cores and up to 8 NVIDIA GPUs (K80).
Microsoft Azure: You can install Keras with a CNTK backend on a Microsoft Azure machine instance of the Microsoft Data Science Virtual Machine family, using only CPU or up to four K80 GPUs.
Use Keras as an API for other frameworks
Keras layers and models are fully compatible with pure TensorFlow tensors; therefore, Keras provides TensorFlow with good model definition attachments. You can even use Keras and other TensorFlow libraries at the same time. Keras is now an official part of the TensorFlow core. For more information, read this blog post.
Switching from the TensorFlow back end to one of the other officially supported Keras back ends is as simple as making a change in the JavaScript Object Notation (JSON) configuration file. For more information, see the Keras documentation.
Currently, Keras can be used as an API for these frameworks:
Keras and Theano. The Theano, which was recently phased out, was Keras's first back-end and was replaced by TensorFlow. TensorFlow supports most of Theano's Keras models. To use the GPU to run the Theano backend, follow the section on Theano in this document.
Keras and CNTK. Keras support for the Microsoft Cognitive Toolkit (CNTK) backend is still in beta. You can read the Microsoft documentation for more details and information.
Keras and Deeplearning4j. Deeplearing4j can use its deeplearing4j-modelimport module to import most Keras models. Deeplearning4j now supports the import of model information about levels, losses, activations, initialization procedures, regularization items, constraints, metrics, and optimizer. For more information, visit the Deeplearning4j documentation.
Keras and Apache MXNet. Keras support for the Apache MXNet backend is still in early testing. This is a work led by the Distributed (Deep) Machine Learning Community. This is gradually becoming another officially supported Keras back end. The code for this backend is provided in this GitHub repository.
Conclusion
Keras differs from other deep learning frameworks. By design, it is intended to be an API specification for neural network modeling. It can serve as a user interface, and it can also extend the capabilities of other deep learning framework backends in which it runs.
The Keras API has become part of Google TensorFlow. Keras also officially supports CNTK, Deeplearning4j, and will soon support Apache MXNet.
Because of this extensive support, Keras has become a practical tool for realizing the migration between frameworks. Developers can not only transplant deep learning neural network algorithms and models, but also transplant pre-trained networks and weights.
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