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Keras 3.0 Tutorial: End-to-End Deep Learning Project Guide
Implement an encoder-decoder recurrent network from scratch
Introduction
Even though I started using Pytorch a while ago, I still miss the concise code style of Keras and the good old days when you could implement a neural network model in just a few lines of code.
So, I was thrilled when Keras announced last November that in addition to TensorFlow, it now also supports Pytorch and Jax as backends!
However, things weren’t perfect: since Keras 3.0 was released not long ago, the related tutorials and documentation hadn’t caught up, and I encountered some troubles during the code migration.
Luckily, after some effort, I can now smoothly use version 3.0 for various end-to-end model developments.
In this article, I’ll share some practical experiences with Keras 3.0 to help you avoid some detours. I’ll use a typical encoder-decoder recurrent neural network as an example to explain how to complete an end-to-end project from scratch using the subclassing API of Keras 3.0, and discuss details to consider when using Pytorch as the backend.
Let’s get started.
