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.

## 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.

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