Metadata-Version: 2.1
Name: tf-keras-vis
Version: 0.6.0
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
Description: # tf-keras-vis
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        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
        
        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 Optimizers embedded in tf.keras to process Activation maximization.
        
        
        ## 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.
        
        ### Guides (ToDo)
        
        * 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
        
        
        ## ToDo
        
        * Guide documentations
        * API documentations
        * We're going to add some methods such as below.
           - Deep Dream
           - Style transfer
        
        
        ## 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.
        
        
        ## 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++.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6, <3.10
Description-Content-Type: text/markdown
Provides-Extra: develop
Provides-Extra: examples
