Neural Nets On Spark

Nisha Muktewar and Seth Hendrickson show how to use Deeplearning4j to build deep learning models on Hadoop and Spark:

Modern convolutional networks can have several hundred million parameters. One of the top-performing neural networks in the Large Scale Visual Recognition Challenge (also known as “ImageNet”), has 140 million parameters to train! These networks not only take a lot of compute and storage resources (even with a cluster of GPUs, they can take weeks to train), but also require a lot of data. With only 30000 images, it is not practical to train such a complex model on Caltech-256 as there are not enough examples to adequately learn so many parameters. Instead, it is better to employ a method called transfer learning, which involves taking a pre-trained model and repurposing it for other use cases. Transfer learning can also greatly reduce the computational burden and remove the need for large swaths of specialized compute resources like GPUs.

It is possible to repurpose these models because convolutional neural networks tend to learn very general features when trained on image datasets, and this type of feature learning is often useful on other image datasets. For example, a network trained on ImageNet is likely to have learned how to recognize shapes, facial features, patterns, text, and so on, which will no doubt be useful for the Caltech-256 dataset.

This is a longer post, but on an extremely interesting topic.

Related Posts

MRAppMaster Errors Running MapReduce Jobs

I have a post looking at potential causes when PolyBase MapReduce jobs are unable to find the MRAppMaster class: Let me tell you about one of my least favorite things I like to see in PolyBase: Error: Could not find or load main class This error is not limited to PolyBase but is instead […]

Read More

Database-First or Kafka-First for Event Streaming

Gwen Shapiro takes us through a scenario where database-first writes for event streaming makes the most sense: Note that the DB does quite a lot for you: it enforces serializability, locks, your logical constraints, etc. If the DB is distributed (Vitesse, Cockroach, Spanner, Yugabyte), it does even more. If you were to go Kafka-first… well, […]

Read More


June 2017
« May Jul »