Keras data augmentation for unbalanced class

#KERAS DATA AUGMENTATION FOR UNBALANCED CLASS HOW TO#
#KERAS DATA AUGMENTATION FOR UNBALANCED CLASS CODE#

How sampling affects the learning processД®ven though the learned function will, in theory, be no different just because the data distribution changes (as long as the training examples carry enough information), a non-ideal learning setup can be unhelpfully slow to learn in.

One of the prerogative powers of an ML practitioner is being able to alter the distribution of learning examples to create an ideal learning environment. However, it very much affects the learning procedure when approximating that function. In general, a dataset may overrepresent certain regions of the X or Y domain compared to the real process, and the choice of what we train a model with may be different again.ДЄn uneven sample of X and Y does not affect the nature of the implicit function linking them. The distribution of examples is normally a secondary concern, but can be worth a momentвЂ™s thought. In supervised learning problems, we care about the relationship between inputs X and outputs Y. We also presented this work at the OвЂ™Reilly TensorFlow World conference in Santa Clara (October 2019) and you can find the presentation here.

#KERAS DATA AUGMENTATION FOR UNBALANCED CLASS CODE#

WeвЂ™ve developed a TensorFlow sampling module coupled with two real-world examples, you can find the code on GitHub. As a side effect, these methods grant additional flexibility to the user and reduce data preparation workloads. Conversely, we look at generating these in memory using tf.random and exploiting HashTables where appropriate.

#KERAS DATA AUGMENTATION FOR UNBALANCED CLASS HOW TO#

We first consider how to select from a large dataset containing all possible inputs. But to take advantage of random samples, more must be done. NaГЇvely, a precomputed subsample of data will make for a fast input function. In this post, we explore some sampling techniques in the context of propensity modelling and recommender systems, using tools available in the tf.data API, and present which methods are beneficial with given data and hardware demands. rarity of positives in a binary classification problem, or a sparse user-item interaction matrix) or to augment the data stored on file it may simply be a matter of efficiency. At training time this may be to deal with class imbalance (e.g. Many real-world machine learning applications require generative or reductive sampling of data. Sampling Methods within TensorFlow Input Functions

Keras data augmentation for unbalanced class