This repository implements techniques to visualize neural networks in Keras. The library currently supports the following techniques:

• Visualization of convolutional and pooling filters [1]
• Visualization of convolutional and pooling activations
• Nearest neighbors in feature space [2]
• Maximal image generation [3]
• Vanilla saliency maps [3]
• Smooth Grad saliency maps [4]
• Integrated Gradients saliency maps [5]
• Grad-CAM saliency maps [6]

Some of these techniques are only applicable to convolutional network and layers. This is spelled out explicitly in the documentation.

The visualization.py module contains functions to directly display the output of each of the above technique using matplotlib. In alternative, the package saliency and the module activation_maximization can be used to obtain raw saliency maps and maximal images as numpy array.

This library is compatible and has been tested with python 3.7 and python 2.7.

Please refer to the Juptyer notebook for example usage.

• tensorflow
• keras
• pillow
• matplotlib
• sklearn

[1] M. D. Zeiler, R. Fergus. Visualizing and Understanding Convolutional Networks. https://arxiv.org/abs/1311.2901

[2] A. Krizhevsky , I. Sutskever , G. E. Hinton. Imagenet classification with deep convolutional neural networks. https://dl.acm.org/citation.cfm?id=3065386

[3] K. Simonyan, A. Vedaldi, A. Zisserman. _Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. https://arxiv.org/abs/1312.6034

[4] D. Smilkov, N. Thorat, B. Kim, F. Viégas, M. Wattenberg. SmoothGrad: removing noise by adding noise. https://arxiv.org/abs/1706.03825

[5] M. Sundararajan, A. Taly, Q. Yan. Axiomatic Attribution for Deep Networks. https://arxiv.org/abs/1703.01365

[6] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. https://arxiv.org/abs/1610.02391