To learn how to utilize Keras for feature extraction on large datasets, just keep reading!

The rest of today’s tutorial will show you how to do exactly that. Most people don’t have 40GB+ of RAM in their machines, so in those situations, we need to be able to perform incremental learning and train our model on incremental subsets of the data. If we had 50,000 of such 100,352-dim feature vectors (assuming 32-bit floats), then we would need a total of 40.14GB of RAM to store the entire set of feature vectors in memory! The problem with that method is that it assumes that all of our extracted features can fit into memory - that may not always be the case!įor example, suppose we have a dataset of 50,000 images and wanted to utilize the ResNet-50 network for feature extraction via the final layer prior to the FC layers - that output volume would be of size 7 x 7 x 2048 = 100,352-dim. Using this method we were able to utilize CNNs to recognize classes it was never trained on! Last week we discussed how to perform transfer learning using Keras - inside that tutorial we focused primarily on transfer learning via feature extraction. Part 3: Fine-tuning with Keras and Deep Learning (next week’s tutorial).Part 2: Keras: Feature extraction on large datasets (today’s post).Part 1: Transfer Learning with Keras and Deep Learning (last week’s tutorial).Today is part two in our three-part series on transfer learning with Keras: You’ll utilize ResNet-50 (pre-trained on ImageNet) to extract features from a large image dataset, and then use incremental learning to train a classifier on top of the extracted features. In this tutorial, you will learn how to use Keras for feature extraction on image datasets too big to fit into memory. Click here to download the source code to this post

Pandian stores-27-05-2019