Image Clustering With Keras And R

Shirin Glander shows us how to use R to extract learned features from Keras and cluster those features:

For each of these images, I am running the predict() function of Keras with the VGG16 model. Because I excluded the last layers of the model, this function will not actually return any class predictions as it would normally do; instead we will get the output of the last layer: block5_pool (MaxPooling2D).

These, we can use as learned features (or abstractions) of the images. Running this part of the code takes several minutes, so I save the output to a RData file (because I samples randomly, the classes you see below might not be the same as in the sample_fruits list above).

Read the whole thing.

Related Posts

Sentiment Analysis with Spark on Qubole

Jonathan Day, et al, have a tutorial on using Qubole to build a sentiment analysis model: This post covers the use of Qubole, Zeppelin, PySpark, and H2O PySparkling to develop a sentiment analysis model capable of providing real-time alerts on customer product reviews. In particular, this model allows users to monitor any natural language text […]

Read More

Running Spark MLlib to Feed Power BI

Brad Llewellyn shows how you can take Spark MLlib results and feed them into Power BI: MLlib is one of the primary extensions of Spark, along with Spark SQL, Spark Streaming and GraphX.  It is a machine learning framework built from the ground up to be massively scalable and operate within Spark.  This makes it […]

Read More


October 2018
« Sep Nov »