[AI] Sequential, Functional, Model sub-classing using Keras and TensorFlow 2.0

 

Keras and TensorFlow 2.0 provide you with three methods to implement your own neural network architectures:

1. Sequential API
2. Functional API
3. Model subclassing

Summary

In this tutorial you learned the three ways to implement a neural network architecture using Keras and TensorFlow 2.0:

• Sequential: Used for implementing simple layer-by-layer architectures without multiple inputs, multiple outputs, or layer branches. Typically the first model API you use when getting started with Keras.
• Functional: The most popular Keras model implementation API. Allows everything inside the Sequential API, but also facilitates substantially more complex architectures which include multiple inputs and outputs, branching, etc. Best of all, the syntax for Keras’ Functional API is clean and easy to use.
• Model subclassing: Utilized when a deep learning researcher/practitioner needs full control over model, layer, and training procedure implementation. Code is verbose, harder to write, and even harder to debug. Most deep learning practitioners won’t need to subclass models using Keras, but if you’re doing research or custom implementation, model subclassing is there if you need it!

If you’re interested in diving head-first into the world of computer vision/deep learning and discovering how to:

• Design and train Convolutional Neural Networks for your project on your own custom datasets.
• Learn deep learning fundamentals, rules of thumb, and best practices.
• Replicate the results of state-of-the-art papers, including ResNet, SqueezeNet, VGGNet, and others.
• Train your own custom Faster R-CNN, Single Shot Detectors (SSDs), and RetinaNet object detectors.
• Use Mask R-CNN to train your own instance segmentation networks.

# 참고 : https://www.pyimagesearch.com/2019/10/28/3-ways-to-create-a-keras-model-with-tensorflow-2-0-sequential-functional-and-model-subclassing/

3 ways to create a Keras model with TensorFlow 2.0 (Sequential, Functional, and Model Subclassing) - PyImageSearch