Explain the Architecture of Keras Framework?
Keras is an Open Source Neural Network library written in Python that runs on top of Theano or TensorFlow. It is designed to be modular, fast and easy to use. Keras is high-level API wrapper for the low-level API, capable of running on top of TensorFlow, CNTK, or Theano.
Keras High-Level API handles the way we make models, defining layers, or set up multiple input-output models. In this level, Keras also compiles our model with loss and optimizer functions, training process with fit function. Keras doesn’t handle Low-Level API such as making the computational graph, making tensors or other variables as it has been handled by the “backend” engine.
Backend is a term in Keras that performs all low-level computation such as tensor products, convolutions and many other things with the help of other libraries such as TensorFlow or Theano. So, the “backend engine” will perform the computation and development of the models. TensorFlow is the default “backend engine” but we can change it in the configuration.