Keras user-friendly, modular expansion is easy.
Keras does not handle low-level calculation. Instead, it uses another library.
Neural layers are independent modules that you can combine to create new models with cost functions, optimizers, initiation schemes, activation functions, and layout schemes. New modules and functions are easy to add. Models are defined in Python code, not separate model configuration files.
Why Keras Is Used
The biggest reasons for using Keras are primarily due to their principles of being user-friendly. Beyond keras learning ease and the ease of modeling, wide-ranging adoption supports a wide range of export options.
Keras is powered by Google, Microsoft, Amazon, Apple, Nvidia, Uber.
Keras does not conduct its own low-level operations such as proper tenor products and convolutions; it uses a backend engine for this.
Model The core keras is a data structure. Two main models are available in Keras :
Sequential model and functional API.
Keras Functional API
The Keras Sequential model is simple, but the model topology is limited. Keras is useful for creating multi-input/multi-output models with common layers such as functional API, directed asiklic graphics (DWids), and complex models as models.
The functional API uses the same layers as the Sequential model, but provides more flexibility in putting them together. In the functional API, it first defines layers, then creates, says, and shrinks the model.
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