<commit_before>PyQt5 visualization of Deep Neural Networks (Development stage)
===============================================================
One can use this code to visualize DNNs (activations, filters, network structure).

Main goal of the toolbox is to visualize networks that solve image classifications tasks. Current version assumes to be given a model trained and saved using [Keras: The Python Deep Learning library](https://keras.io/ "Keras's Homepage")
Development of this toolbox was motivated by the paper:

 - <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. [Understanding neural networks through deep visualization](http://arxiv.org/abs/1506.06579/ "Computer Science > Computer Vision and Pattern Recognition") Presented at the Deep Learning Workshop, International Conference on Machine Learning (ICML), 2015.


 Functionality
 =============

Example of a network visualization that classifies different shapes 
![alt text](https://github.com/Petr-By/qtpyvis/blob/master/docs/example_view.png "Example")

The main window consists of activation maps for selected layer, the input and the structure of a network where the layers of interest can be chosen

Installation
============
<commit_msg>Fix link<commit_after>PyQt5 visualization of Deep Neural Networks (Development stage)
===============================================================
One can use this code to visualize DNNs (activations, filters, network structure).

Main goal of the toolbox is to visualize networks that solve image classifications tasks. Current version assumes to be given a model trained and saved using [Keras: The Python Deep Learning library](https://keras.io/ "Keras's Homepage")
Development of this toolbox was motivated by the paper:

 - <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. [Understanding neural networks through deep visualization](http://arxiv.org/abs/1506.06579/ "Computer Science > Computer Vision and Pattern Recognition") Presented at the Deep Learning Workshop, International Conference on Machine Learning (ICML), 2015.


 Functionality
 =============

Example of a network visualization that classifies different shapes 
![alt text](https://github.com/Petr-By/qtpyvis/blob/master/docs/example.png)

The main window consists of activation maps for selected layer, the input and the structure of a network where the layers of interest can be chosen

Installation
============

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