The sophistication of the leading frameworks has prevented developers new to machine learning from using them. Several recommendations for enhanced and simpler high-level APIs for generating neural network models have been made, all of which appear similar at first glance but differ when examined more closely.
Keras is one of the most popular APIs for high-level neural networks. It’s developed in Python and works with a variety of neural network computation engines on the back end.
Keras appears to be a winner, if not necessarily the winner, given that the TensorFlow team has accepted it as the high-level API for the upcoming TensorFlow 2.0 release.
Keras was designed to be user-friendly, modular, extensible, and Python-compatible.
Individual modules like neural layers, cost functions, optimizers, initialization methods, activation functions, and regularization techniques can be combined to build new ones.
New modules, like new classes and methods, are straightforward to add. Models are defined in Python code rather than individual model configuration files.
Why should you use Keras?
Keras’ guiding principles, particularly the one about being user-friendly, are the most compelling reasons to adopt them. Keras has a wide range of production deployment choices, integration with at least five back-end engines- TensorFlow, Theano, CNTK, PlaidML, and MXNet, and strong support for multiple GPUs and distributed training, in addition to its ease of learning and model development. In addition, Microsoft, Google, Microsoft, Apple, Amazon, Nvidia, and others have funded Keras.
- Keras is an API that was designed to be simple to learn. Keras was designed to be straightforward. It provides uniform and straightforward APIs, minimizes the number of steps necessary to create common code, and explicitly explains user error.
- In Keras, prototyping takes less time. As a result, your ideas will be implemented and delivered in a shorter time. Keras also offers a choice of deployment methods according to the demands of the user.
- Languages with a high degree of abstraction and built-in features are sluggish, and it might be difficult to add additional features to them. Keras, on the other hand, is a fast framework that works on top of TensorFlow. Keras is also tightly connected with TensorFlow, allowing you to quickly construct bespoke processes.
- Keras has a large and well-developed research community. Compared to other deep learning frameworks, the documentation and support are significantly more detailed.
- Keras is utilized commercially by several firms, including Netflix, Uber, Square, Yelp, and others, who have released Keras-based products into the public domain.
Apart from that, Keras has the following features:
- It operates without a hitch on both the CPU and GPU.
- Almost all neural network models are supported.
- It is modular, making it expressive, adaptable, and well-suited to cutting-edge research.
- Create a network: In this stage, you’ll define the various layers in your model as well as their connections. Sequential and functional models are the two major types of models in Keras. The dataflow between the models is defined once you specify whatever sort of model you want.
- Assemble a network: Compiling code is converting it into a format that a machine can comprehend. This is done in Keras using the model.compile() method. To assemble the model, you must first specify the loss function, which calculates your model’s losses, the optimizer, which decreases the loss, and the metrics, which are used to determine your model’s correctness.
- Adapt to the network: After compiling, use this to fit our model to our data. On your data, this is utilized to train the model.
- Evaluate the network: After you’ve fitted your model, you’ll need to assess its inaccuracy.
- Model.predict() is used to create predictions based on fresh data using your model.
- Keras is a programming language that may be used to create deep models that can be utilized on smartphones.
- Keras is also used for distributed deep learning model training.
- Uber, Netflix, Yelp, and other businesses employ Keras.
- Keras may also be used to create global deep learning models.
- Keras is also often used in deep learning contests to develop and deploy working models in a short period.