Metadata-Version: 2.1
Name: deep_plots
Version: 0.1.1
Summary: Visualize Your Deep Learning Training in Static Graphics
Home-page: https://github.com/jfilter/deep-plots
Author: Johannes Filter
Author-email: hi@jfilter.de
License: MIT
Description: # Deep Plots [![Build Status](https://travis-ci.com/jfilter/deep-plots.svg?branch=master)](https://travis-ci.com/jfilter/deep-plots) [![PyPI](https://img.shields.io/pypi/v/deep-plots.svg)](https://pypi.org/project/deep-plots/) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/deep-plots.svg)](https://pypi.org/project/deep-plots/)
        
        Visualize Your Deep Learning Training in Static Graphics.
        
        <div align="center">
          <img src="demo/loss.png" alt="Plot Loss">
        </div>
        
        **Why?** [Analyzing learning curves](https://www.coursera.org/lecture/machine-learning/learning-curves-Kont7) are a standard way to evaluate the learning performances of machine learning models. There exist [several tools](#Related) for creating live plots. This Python package focuses on producing beautiful static graphics only.
        
        Currently, only plotting from [Keras CSV log file](https://keras.io/callbacks/#csvlogger) format is supported.
        
        For creating the graphics, [plotnine](https://github.com/has2k1/plotnine) is used which is build upon [Matplotlib](https://matplotlib.org/).
        
        ## Installation
        
        ```bash
        pip install deep_plots
        ```
        
        Unfortunately, you may need to:
        
        ```bash
        pip install numpy
        ```
        
        before because a [depedency implicitly assumes numpy is installed](https://github.com/statsmodels/statsmodels/issues/3207).
        
        ## Usage
        
        ```python
        # create a Keras callback to log your training
        csv_logger = keras.callbacks.CSVLogger('log.csv')
        
        # train your model
        model.fit(X, y, ..., callbacks=[csv_logger, ...])
        
        # after finishing training, plot the learning curves with Deep Plots
        deep_plots.from_keras_log('log.csv', 'output_dir')
        ```
        
        ## Related
        
        -   [TensorBoard](https://github.com/tensorflow/tensorboard): Live plots for TensorFlow, [Keras](https://keras.io/callbacks/#tensorboard).
        -   [tensorboardX](https://github.com/lanpa/tensorboardX): Live plots for PyTorch, Chainer etc..
        -   [Live Loss Plot](https://github.com/stared/livelossplot): Live plots in Jupyter Notebooks for Keras, PyTorch etc..
        
        ## Contributing
        
        If you have a **question**, found a **bug** or want to propose a new **feature**, have a look at the [issues page](https://github.com/jfilter/deep-plots/issues).
        
        **Pull requests** are especially welcomed when they fix bugs or improve the code quality.
        
        ## License
        
        MIT.
        
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 2.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 3 - Alpha
Description-Content-Type: text/markdown
