Metadata-Version: 1.1
Name: ann_visualizer
Version: 1.2
Summary: A python library for visualizing Keras Artificial Neural Networks
Home-page: https://github.com/Prodicode/ann-visualizer
Author: Tudor Gheorghiu
Author-email: tudor.posta@live.com
License: MIT
Description: ![photo](https://i.imgur.com/DrZJOzy.png)
        ![photo](https://i.imgur.com/EHIoNoR.png)
        
        # ANN Visualizer
        [![PyPI version](https://badge.fury.io/py/ann_visualizer.png)](https://badge.fury.io/py/ann_visualizer) 
        
        A great visualization python library used to work with Keras. It uses python's graphviz library to create a presentable graph of the neural network you are building.
        
        ## Installation
        ### From Github
        1. Download the `ann_visualizer` folder from the github repository.
        2. Place the `ann_visualizer` folder in the same directory as your main python script.
        
        ### From pip
        Use the following command:
        
        ```bash
        pip install ann_visualizer
        ```
        
        ## Usage
        
        ```python
        
        from ann_visualizer.visualize import ann_viz;
        #Build your model here
        ann_viz(model)
        ```
        
        ## Documentation
        
        ### ann_viz(model, view=True, filename="network.gv")
        * `model` - The Keras Sequential model
        * `view` - If True, it opens the graph preview after executed
        * `filename` - Where to save the graph. (.gv file format)
        
        ## Example
        ```python
        import keras;
        from keras.models import Sequential;
        from keras.layers import Dense;
        
        network = Sequential();
                #Hidden Layer#1
        network.add(Dense(units=6,
                          activation='relu',
                          kernel_initializer='uniform',
                          input_dim=11));
        
                #Hidden Layer#2
        network.add(Dense(units=6,
                          activation='relu',
                          kernel_initializer='uniform'));
        
                #Exit Layer
        network.add(Dense(units=1,
                          activation='sigmoid',
                          kernel_initializer='uniform'));
        
        from ann_visualizer.visualize import ann_viz;
        
        ann_viz(network);
        ```
        
        This will output:
        ![photo](https://i.imgur.com/ngThGlk.png)
        
        ## Contributions
        This library is still unstable. Please report all bug to the issues section. It is currently tested with `python3.5`, but it should run just fine on any python3.
        
Keywords: ann,ai,visualizer,learning,artificial,intelligence
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
