In this tutorial, I show how to share neural network layer weights and define custom loss functions. The example code assumes beginner knowledge of Tensorflow 2 and the Keras API.
For a recent project, I wanted to use Tensorflow 2 / Keras to re-implement DeepKoopman, an autoencoder-based neural network architecture described in “Deep learning for universal linear embeddings of nonlinear dynamics”. My end goal was to create a user-friendly version that I could eventually extend
DeepKoopman embeds time series x onto data into a low-dimensional coordinate system y in which the dynamics are linear.
The DeepKoopman schematic shows that there are three main components:
#tensorflow #machine-learning #python #neural-networks #keras