Keras, initially developed as part of a research project called ONEIROS, is a high-level open-source neural network API, written in Python and capable of running on TensorFlow, CNTK, Theano. Keras was designed to allow rapid experimentation with deep neural networks.
The deep learning library focuses on being minimal, modular and extensible, as well as efficiently grouping numerical computing libraries like Theano and TensorFlow. It allows its users to define and train neural network models in just a few short lines of code.
The programming interface of the Keras application is very familiar to anyone who has worked with the well-known and accepted Scikit learning API. Keras supports convolutional and recurring networks, as well as combinations of both. Works without major CPU and GPU efforts.
The features of your library include numerous implementations of building blocks of widely used neural networks, such as layers, objectives, activation functions, optimizers and also several tools to work with image and text data more quickly.
The Keras library follows best practices to reduce cognitive load and therefore offers consistent and straightforward APIs, minimizing the number of user actions required for daily use cases and also providing clear and actionable feedback in the event of an error. The model can be understood as a graph or a sequence of fully configurable independent modules that can be connected with the least possible restrictions.
For example, optimizers, initialization schemes, neural layers, smoothing schemes, cost functions, activation functions, are all independent modules that can be combined to create new models.
New modules prove to be simpler to add new classes and functions, and the modules that already exist provide many examples. The ability to create new modules easily allows the user full expressiveness, making the library suitable for advanced searches. There is no separate model configuration file in the declarative format. The models were described in Python, which is compact, more comfortable to debug and also allows for ease of extensibility.
Therefore, we can conclude by saying that Keras is deep learning in Python, through which one can learn to pre-process data, model, evaluate and optimize neural networks. The Deep Learning Library turns out to be a high-level alternative to TensorFlow and Theano. Therefore, this library focuses on the structure of the model, avoiding the complexity of numerical programming on GPUs.
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