How to Visualize Filters and Feature Maps in Convolutional Neural Networks
Last Updated on July 5, 2019
Deep learning neural networks are generally opaque, meaning that although they can make useful and skillful predictions, it is not clear how or why a given prediction was made.
Convolutional neural networks, have internal structures that are designed to operate upon two-dimensional image data, and as such preserve the spatial relationships for what was learned by the model. Specifically, the two-dimensional filters learned by the model can be inspected and visualized to discover the types of features that the model will detect, and the activation maps output by convolutional layers can be inspected to understand exactly what features were detected for a given input image.
In this tutorial, you will discover how to develop simple visualizations for filters and feature maps in a convolutional neural network.
After completing this tutorial, you will know:
- How to develop a visualization for specific filters in a convolutional neural network.
- How to develop a visualization for specific feature maps in a convolutional neural network.
- How to systematically visualize feature maps for each block in a deep convolutional neural network.
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