Metadata-Version: 2.1
Name: Model-Visual
Version: 1.1.1
Summary: A deep learning model visualization suite based on Mermaid
Home-page: UNKNOWN
Author: YuAn, Lu
Author-email: luyuam0@gmail.com
License: UNKNOWN
Description: 
        # Model Visual
        
        ![GitHub](https://img.shields.io/github/license/YuamLu/ModelVisual)
        ![GitHub](https://img.shields.io/badge/powered%20by-YuAN%20Lu-orange)
        ![Github stars](https://img.shields.io/github/stars/YuamLu/ModelVisual.svg)
        
        ⭐ Star us on GitHub — it motivates us a lot!
        
        [Model Visual](https://github.com/YuamLu/ModelVisual) is a lightweight deep learning model visualization suite based on [Mermaid](https://github.com/mermaid-js/mermaid).This kit will convert model schemas into Mermaid code and output code snippets or complete web pages. Users can easily adjust the color and border of the chart.
        
        > Only support tensorflow now. More frameworks will comming soon!
        
        ## Contents
        
        - [Model Visual](#model-visual)
          - [Contents](#contents)
          - [Demo](#demo)
          - [Tutorial](#tutorial)
            - [Isnstallation](#isnstallation)
            - [Create object](#create-object)
            - [Run and get result](#run-and-get-result)
          - [APIs](#apis)
        
        ## Demo
        
        This is the suite's demo. Suite will generate a web page that include a chart.
        
        ![Demo img](https://github.com/YuamLu/ModelVisual/raw/master/Images/Demo_web.png)
        
        ## Tutorial
        
        ### Isnstallation
        
        First,install the suite by using pip.
        
        ```
        pip install model-visual
        ```
        
        ### Create object
        
        Then,creat an object and set some parameter.
        
        Test is the name of an object, you can take any name you want. And model is your keras model's name.(Notice: the model has to be compiled)
        
        ```
        from model_visual import ModelVisual
        
        test = ModelVisual(model)
        ```
        
        ### Run and get result
        
        ```
        test.run()
        test.save_web_page()
        ```
        
        Beside ``save_web_page()``,you can also use ``return_js_code()`` or ``return_web_page()``.
        
        ``save_web_page()`` will create a html file,
         ``return_js_code()`` and ``save_web_page()`` will return you sourse code.
        
        ## APIs
        
        ```
        
        set_name(): # set the html file name
        
        set_path(): # set the html path name
        
        set_chart_fill_color(): # set the cart fill color
        
        set_chart_stroke_color(): # set the chart strole color
        
        set_chart_stroke_width(): # set the chart stroke width
        
        set_model(): # set the keras model
        
        ```
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
