Keras

Understanding Variational Autoencoders and Implementation in Keras

Variational Autoencoders (VAEs)[Kingma, et.al (2013)] let us design complex generative models of data that can be trained on large datasets. This post is about understanding the VAE concepts, its loss functions and how we can implement it in keras. Generating data from a latent space VAEs, in terms of probabilistic terms, assume that the data-points in a large dataset are generated from a latent space. For e.g., let us assume we want to generate the image of an animal. First we imagine that it has four legs, a head and a tail. This is analogous to the latent space and from this set of characteristics that are defined in the latent space, the model will learn to generate the image of an animal.

Tuesday, July 2, 2019 | 10 minutes Read

Tracking Multiple Losses with Keras

Often we deal with networks that are optimized for multiple losses (e.g., VAE). In such scenarios, it is useful to keep track of each loss independently, for fine-tuning its contribution to the overall loss. This post details an example on how to do this with keras. Let us look at an example model which needs to trained to minimize the sum of two losses, say mean square error (MSE) and mean absolute error (MAE). Let $\lambda_{mse}$ be the hyperparameter that controls the contribution of MSE to the toal loss. i.e., the total loss is MAE + $\lambda_{mse}$ * MSE. This loss can be implemented using:

Monday, March 4, 2019 | 2 minutes Read