Metadata-Version: 2.1
Name: keras-hist-graph
Version: 0.0.3
Summary: Graph Keras training history object
Home-page: https://github.com/whyboris/keras-hist-graph
Author: Boris Yakubchik
Author-email: yboris@yahoo.com
License: MIT
Description: # Keras History Graph
        
        Uses `matplotlib` to generate a simple graph of the history object. Particularly useful with _Jupyter_
        
        It will show the _accuracy_ and _loss_ for both _training data_ and _validation data_.
        It will also print the maximum _validation accuracy_ reached during the training.
        
        ![Example output](https://user-images.githubusercontent.com/17264277/43170872-5ff85946-8f75-11e8-86e8-d08a0fa79f15.png)
        
        # Installation
        
        `pip install keras-hist-graph`
        
        # Usage
        
        Requires _Keras_
        
        ```py
        from keras_hist_graph import plot_history
        
        history = model.fit(x, y, ...)
        
        plot_history(history)
        ```
        
        # Arguments
        
        _plot_history_ now accepts any of these arguments (in any order)
        
        | argument | default | possible | details |
        | -------- | ------- | -------- | ------- |
        | fig_size | (10, 6) | (`float`, `float`) | Indicates _width_ and _height_ of the resulting graph |
        | min_accuracy | 0.5 | `[0, 1)` | Minimum accuracy to graph (often we don't care if acuracy is below 50%) |
        | smooth_factor | 0.75 | `[0, 1]` | Zero to one, inclusive. Smooths out the curves by averaging previous points. Consider makeing smaller if number of epochs is small. |
        | start_epoch | 5 | integer >= 0 | Plot the history starting at this epoch. Useful since the first epochs can have very high loss that makes the later loss hard to analyze visually |
        | xkcd | True | `True` `False` | Whether to render in the _XKCD_ style. You might need to render twice for all properties to update if you change the boolean after using the method before |
        
        Example:
        
        ```py
        plot_history(history, fig_size = (11, 8.5), min_accuracy = 0.8, start_epoch = 2, smooth_factor = 0.1)
        ```
        
        ### Notes
        
        [Why use the XKCD style?](https://www.chrisstucchio.com/blog/2014/why_xkcd_style_graphs_are_important.html)
        
        It’s a great way to communicate the imprecision of the underlying data!
        
Keywords: keras,jupyter
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
