{"id":8872,"date":"2021-03-11T17:09:02","date_gmt":"2021-03-11T11:39:02","guid":{"rendered":"https:\/\/www.h2kinfosys.com\/blog\/?p=8872"},"modified":"2025-09-30T07:03:59","modified_gmt":"2025-09-30T11:03:59","slug":"creating-deep-learning-model-with-keras","status":"publish","type":"post","link":"https:\/\/www.h2kinfosys.com\/blog\/creating-deep-learning-model-with-keras\/","title":{"rendered":"Creating Deep Learning Model with Keras Functional API"},"content":{"rendered":"\n

In the last tutorial, it was said that there are two ways of creating deep learning models in Keras. The first was by using the sequential class where layers of the network are stacked on each other. Although these models are common amongst learners, they, however, have limitations. For instance, with the sequential model, the Deep learning architecture<\/a> cannot be made to share two different layers or have more than one input or output. In real-life applications, this is a bottleneck.<\/p>\n\n\n\n

The good news is that the second method; using the functional API, solves this problem. The functional API gives room for more flexibility in the model architecture and is typically ideal for a more complex model. In this tutorial, we shall talk about the Keras functional API<\/a> and go further to implement it using the same example in the last tutorial where the sequential model was used. By the end of this tutorial you will learn the following:<\/p>\n\n\n\n

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