Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (pypi.python.org).
Help us improve Python packaging - Donate today!

Saliency methods for TensorFlow

Project Description

# Saliency Methods

## Introduction

This repository contains code for [SmoothGrad](https://pair-code.github.io/saliency/), as well as implementations of
several other saliency techniques. Each of these techniques can also be
augmented with SmoothGrad. The techniques implemented in this library are:

* Vanilla Gradients
([paper](https://scholar.google.com/scholar?q=Visualizing+higher-layer+features+of+a+deep+network&btnG=&hl=en&as_sdt=0%2C22),
[paper](https://arxiv.org/abs/1312.6034))
* Guided Backpropogation ([paper](https://arxiv.org/abs/1412.6806))
* Integrated Gradients ([paper](https://arxiv.org/abs/1703.01365))
* Occlusion

This list is by no means comprehensive. We are accepting pull requests to add
new methods!

## Download
```
git clone https://github.com/pair-code/saliency
cd saliency
```

## Usage

Each saliency mask class extends from the `SaliencyMask` base class. This class
contains the following methods:

* `__init__(graph, session, y, x)`: Constructor of the SaliencyMask. This can
modify the graph, or sometimes create a new graph. Often this will add nodes
to the graph, so this shouldn't be called continuously. `y` is the output
tensor to compute saliency masks with respect to, `x` is the input tensor
with the outer most dimension being batch size.
* `GetMask(x_value, feed_dict)`: Returns a mask of the shape of non-batched
`x_value` given by the saliency technique.
* `GetSmoothedMask(x_value, feed_dict)`: Returns a mask smoothed of the shape
of non-batched `x_value` with the SmoothGrad technique.

The visualization module contains two visualization methods:

* ```VisualizeImageGrayscale(image_3d, percentile)```: Marginalizes across the
absolute value of each channel to create a 2D single channel image, and clips
the image at the given percentile of the distribution. This method returns a
2D tensor normalized between 0 to 1.
* ```VisualizeImageDiverging(image_3d, percentile)```: Marginalizes across the
value of each channel to create a 2D single channel image, and clips the
image at the given percentile of the distribution. This method returns a
2D tensor normalized between -1 to 1 where zero remains unchanged.

If the sign of the value given by the saliency mask is not important, then use
```VisualizeImageGrayscale```, otherwise use ```VisualizeImageDiverging```. See
the SmoothGrad paper for more details on which visualization method to use.

## Examples

[This example iPython notebook]([http://github.com/pair-code/saliency/blob/master/Examples.ipynb]) shows
these techniques is a good starting place.

Another example of using GuidedBackprop with SmoothGrad from TensorFlow:

```
from guided_backprop import GuidedBackprop
import visualization

...
# Tensorflow graph construction here.
y = logits[5]
x = tf.placeholder(...)
...

# Compute guided backprop.
# NOTE: This creates another graph that gets cached, try to avoid creating many
# of these.
guided_backprop_saliency = GuidedBackpropSaliency(graph, session, y, x)

...
# Load data.
image = GetImagePNG(...)
...

smoothgrad_guided_backprop =
guided_backprop_saliency.GetSmoothedMask(image, feed_dict={...})

# Compute a 2D tensor for visualization.
grayscale_visualization = visualization.VisualizeImageGrayscale(
smoothgrad_guided_backprop)
```

This is not an official Google product.


Release History

This version
History Node

0.0.2

History Node

0.0.1

Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Hash SHA256 Hash Help Version File Type Upload Date
saliency-0.0.2-py2.py3-none-any.whl
(11.0 kB) Copy SHA256 Hash SHA256
py2.py3 Wheel Jul 11, 2017
saliency-0.0.2.tar.gz
(6.2 kB) Copy SHA256 Hash SHA256
Source Jul 11, 2017

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting