https://github.com/experiencor/deep-viz-keras

Implementations of some popular Saliency Maps in Keras
https://github.com/experiencor/deep-viz-keras

convolutional-neural-networks deep-learning gradient keras machine-learning saliency-map visualization

Last synced: about 1 month ago
JSON representation

Implementations of some popular Saliency Maps in Keras

• Host: GitHub
• URL: https://github.com/experiencor/deep-viz-keras
• Owner: experiencor
• Created: 2017-06-23T08:16:07.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-05-11T13:33:01.000Z (over 5 years ago)
• Last Synced: 2023-11-07T19:01:07.009Z (about 1 year ago)
• Topics: convolutional-neural-networks, deep-learning, gradient, keras, machine-learning, saliency-map, visualization
• Language: Jupyter Notebook
• Homepage: https://experiencor.github.io/cnn_visual.html
• Size: 2.37 MB
• Stars: 165
• Watchers: 8
• Forks: 32
• Open Issues: 9
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        

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 and https://github.com/mbojarski/VisualBackProp.

# Examples

* Dog



* Dog and Cat



# Usage

cd deep-viz-keras

```python

from guided_backprop import GuidedBackprop

from utils import *

from keras.applications.vgg16 import VGG16

# Load the pretrained VGG16 model and make the guided backprop operator

vgg16_model = VGG16(weights='imagenet')

vgg16_model.compile(loss='categorical_crossentropy', optimizer='adam')

guided_bprop = GuidedBackprop(vgg16_model)

# Load the image and compute the guided gradient

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.

# Notes

+ To compute gradient of any output w.r.t. any input https://github.com/experiencor/deep-viz-keras/issues/5#issuecomment-376452683.