What is Keras layers?

The key functionality of layers is analyzing the structure of the data thatis being fed into the neural network. Keras provides different types of standard layers that provide the functionality to perform operations like convolution, pooling, flattening, etc., on input data to generate the expected output. The key parameters that are specified for these layers include dimensions of the input data, which is mandatory and some other optional parameters, including:

• kernel initializer: It specifies the initialization method for the weights of the layer. If not specified a default initializer called ‘glorot_uniform’ is used.
• Activation function: The activationdata, function is specified to introduce non-linearity to the layer. If not specified the layer remains linear. Some common activation functions are relu, softmax, sigmoid, and Leaky Relu.
• Other optional parameters include kernel_constraint for specifying a range of weights and kernel_regularizer for optimization of the layer.

Keras is a powerful API built on top of deep learning libraries like TensorFlow and PyTorch. The Layers API is a key component of Keras, allowing you to stack predefined layers or create custom layers for your model. In this article, we will discuss the Keras layers API.