An accuracy/loss curve plot will be output to a. The model will be trained on the CIFAR-10 dataset. The training script, train.py, will load a model depending on the provided command line arguments. Our models.py contains three functions to build Keras/TensorFlow 2.0 models using the Sequential, Functional and Model subclassing APIs, respectively. Then extract the files and inspect the directory contents with the tree command: $ tree -dirsfirst Go ahead and grab the source code to this post by using the “Downloads” section of this tutorial. Once our training script is implemented we’ll then train each of the sequential, functional, and subclassing models, and review the results.įurthermore, all code examples covered here will be compatible with Keras and TensorFlow 2.0.
#KERAS SEQUENTIAL MODEL HOW TO#
I’ll then show you how to train each of these model architectures. In the first half of this tutorial, you will learn how to implement sequential, functional, and model subclassing architectures using Keras and TensorFlow 2.0. Looking for the source code to this post? Jump Right To The Downloads Section 3 ways to create a Keras model with TensorFlow 2.0 (Sequential, Functional, and Model subclassing)
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