Metadata-Version: 2.1
Name: tf-keras-vis
Version: 0.6.2
Summary: Neural network visualization toolkit for tf.keras
Home-page: https://github.com/keisen/tf-keras-vis
Author: keisen
Author-email: k.keisen@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Environment :: GPU :: NVIDIA CUDA
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6, <3.10
Description-Content-Type: text/markdown
Provides-Extra: develop
Provides-Extra: examples
License-File: LICENSE

# tf-keras-vis
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tf-keras-vis is a visualization toolkit for debugging `tf.keras` models in Tensorflow2.0+.
Currently supported methods for visualization include:

* Activation Maximization
* Class Activation Maps
   - [GradCAM](https://arxiv.org/pdf/1610.02391v1.pdf)
   - [GradCAM++](https://arxiv.org/pdf/1710.11063.pdf)
   - [ScoreCAM](https://arxiv.org/pdf/1910.01279.pdf)
   - [Faster-ScoreCAM](https://github.com/tabayashi0117/Score-CAM/blob/master/README.md#faster-score-cam)
* Saliency Maps
   - [Vanilla Saliency](https://arxiv.org/pdf/1312.6034.pdf)
   - [SmoothGrad](https://arxiv.org/pdf/1706.03825.pdf)

tf-keras-vis is designed to be light-weight, flexible and ease of use.
All visualizations have the features as follows:

* Support **N-dim image inputs**, that's, not only support pictures but also such as 3D images.
* Support **batchwise** processing, so, be able to efficiently process multiple input images.
* Support the model that have either **multiple inputs** or **multiple outputs**, or both.
* Support the **mixed-precision** model. [ * ](#known-issues)

And in Activation maximization,

* Support Optimizers that are built to tf.keras.


## Visualizations

### Visualizing Dense Layer

<img src='https://github.com/keisen/tf-keras-vis/raw/master/examples/images/visualize-dense-layer.png' width='600px' />

### Visualizing Convolutional Filer

<img src='https://github.com/keisen/tf-keras-vis/raw/master/examples/images/visualize-filters.png' width='600px' />

### GradCAM

<img src='https://github.com/keisen/tf-keras-vis/raw/master/examples/images/gradcam_plus_plus.png' width='600px' />

The images above are generated by `GradCAM++`.

### Saliency Map

<img src='https://github.com/keisen/tf-keras-vis/raw/master/examples/images/smoothgrad.png' width='600px' />

The images above are generated by `SmoothGrad`.


## Requirements

* Python 3.6-3.9
* tensorflow>=2.0.2


## Installation

* PyPI

```bash
$ pip install tf-keras-vis tensorflow
```

* Docker (container that run Jupyter Notebook)

```bash
$ cd tf-keras-vis
$ docker build -t <TAG> -f dockerfiles/gpu.Dockerfile .
$ docker run --gpus all --privileged -itd -p 8888:8888 <TAG>
```

Or

```bash
$ docker run --gpus all --privileged -itd -p 8888:8888 keisen/tf-keras-vis:0.5.0-gpu
```

> You can find other images at [Docker Hub](https://hub.docker.com/repository/docker/keisen/tf-keras-vis/tags).


## Usage

Please see below for details:

### Getting Started Guides

* [Saliency and CAMs](https://github.com/keisen/tf-keras-vis/blob/master/examples/attentions.ipynb)
* [Visualize Dense Layer](https://github.com/keisen/tf-keras-vis/blob/master/examples/visualize_dense_layer.ipynb)
* [Visualize Convolutional Filer](https://github.com/keisen/tf-keras-vis/blob/master/examples/visualize_conv_filters.ipynb)

**[NOTE]**
If you have ever used [keras-vis](https://github.com/raghakot/keras-vis), you may feel that tf-keras-vis is similar with keras-vis.
Actually tf-keras-vis derived from keras-vis, and both provided visualization methods are almost the same.
But please note that tf-keras-vis APIs does NOT have compatibility with keras-vis.


## Use Cases

* [chitra](https://github.com/aniketmaurya/chitra)
   * A Deep Learning Computer Vision library for easy data loading, model building and model interpretation with GradCAM/GradCAM++.


## Known Issues

* With InceptionV3, ActivationMaximization doesn't work well, that's, it might generate meaninglessly blur image.
* With cascading model, Gradcam and Gradcam++ don't work well, that's, it might occur some error. So we recommend, in this case, to use FasterScoreCAM.
* `channels-first` models and data is unsupported.
* With a `mixed-precision` model that has a layer which are set float32 dtype exlicitly, ActivationMaximization may raise a error.
* With a `mixed-precision` model, Regurarization values that is calculated by ActivationMaximization may be NaN.


## ToDo

* Guides
   * Visualizing multiple attention or activation images at once utilizing batch-system of model
   * Define various score functions
   * Visualizing attentions with multiple inputs models
   * Visualizing attentions with multiple outputs models
   * Advanced score functions
   * Tuning Activation Maximization
   * Visualizing attentions for N-dim image inputs
* API documentations
* We're going to add some methods such as below.
   - Deep Dream
   - Style transfer


