Keras Layers API (Part 1)

Learn about Keras, one of the most popular DL libraries, and explore its functionality to implement neural network layers.

What is Keras?

Keras is a high-level Python library developed by Francois Chollet, a French AI researcher and software engineer who’s a main contributor to the TF framework. Since Keras is a high-level library, it uses one of the frameworks, such as TF, Microsoft Cognitive Toolkit (CNTK), or Theano, as its backend to perform low-level computations. Its user-friendly syntax facilitates fast prototyping and experimentation with deep neural networks. Using Keras, we can easily create complex deep neural networks, such as Convolutional Neural Networks (CNNs)CNNs use convolutions to capture features, especially in input images. and Recurrent Neural Networks (RNNs)RNNs use feedback paths from output to input and are particularly suitable for sequential input and time-series data. .

Keras plays a key part in the TF2 ecosystem, covering the DL workflow from data management to hyperparameter tuning. Keras reduces the coding effort to build DL models by hiding low-level details. Let’s explore various commonly used neural network layers Keras can create for us.

Keras layers

A neural network consists of interconnected layers. These layers give us tensor(s) as outputs after performing network operations on the input tensor(s).

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