This repository contains the implementations in Keras of various methods to understand the prediction by a Convolutional Neural Networks. Implemented methods are:

• Vanila gradient [https://arxiv.org/abs/1312.6034]
• Guided backprop [https://arxiv.org/abs/1412.6806]
• Integrated gradient [https://arxiv.org/abs/1703.01365]
• Visual backprop [https://arxiv.org/abs/1611.05418]

Each of them is accompanied with the corresponding smoothgrad version [https://arxiv.org/abs/1706.03825], which improves on any baseline method by adding random noise.

Courtesy of https://github.com/tensorflow/saliency, https://github.com/mbojarski/VisualBackProp.

Examples

• Dog

• Dog and Cat

Usage

cd deep-viz-keras

from guided_backprop import GuidedBackprop
from utils import *

guided_bprop = GuidedBackprop(vgg16_model)
image = load_image(/path/to/image)
mask = guided_bprop.get_mask(image)               # compute the gradients
show_image(mask)                                  # display the grayscaled mask

The examples.ipynb contains the demos of all implemented methods using the built-in VGG16 model of Keras.