Metadata-Version: 2.1
Name: livelossplot
Version: 0.3.3
Summary: Live training loss plot in Jupyter Notebook for Keras, PyTorch and others.
Home-page: https://github.com/stared/livelossplot
Author: Piotr Migdał
Author-email: pmigdal@gmail.com
License: MIT
Description: # Live Loss Plot
        
        ![PyPI version](https://img.shields.io/pypi/pyversions/livelossplot.svg)
        ![PyPI license](https://img.shields.io/pypi/l/livelossplot.svg)
        ![PyPI status](https://img.shields.io/pypi/status/livelossplot.svg)
        [![Downloads](http://pepy.tech/badge/livelossplot)](http://pepy.tech/count/livelossplot)
        
        Don't train deep learning models blindfolded! Be impatient and look at each epoch of your training!
        
        A live training loss plot in [Jupyter Notebook](http://jupyter.org/) for [Keras](https://keras.io/), [PyTorch](http://pytorch.org/) and other frameworks. An open source Python package by [Piotr Migdał](http://p.migdal.pl/), [Kasia Kańska and others](https://github.com/stared/livelossplot/graphs/contributors). **Open for collaboration!** (Some tasks are as simple as writing code docstrings, so - no excuses! :))
        
        ```
        from livelossplot.keras import PlotLossesCallback
        
        model.fit(X_train, Y_train,
                  epochs=10,
                  validation_data=(X_test, Y_test),
                  callbacks=[PlotLossesCallback()],
                  verbose=0)
        ```
        
        ![](livelossplot.gif)
        
        So remember, [log your loss](https://twitter.com/pmigdal/status/943764924983017473)!
        
        * (The most FA)Q: Why not TensorBoard?
        * A: Jupyter Notebook compatibility (for exploration and teaching). Simplicity of use.
        
        ## Installation
        
        To install [this verson from PyPI](https://pypi.org/project/livelossplot/), type:
        
        ```
        pip install livelossplot
        ```
        
        To get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type:
        
        ```
        pip install git+git://github.com/stared/livelossplot.git
        ```
        
        ## Examples
        
        Look at notebook files with full working [examples](https://github.com/stared/livelossplot/blob/master/examples/):
        
        * [keras.ipynb](https://github.com/stared/livelossplot/blob/master/examples/keras.ipynb) - a Keras callback
        * [minimal.ipynb](https://github.com/stared/livelossplot/blob/master/examples/minimal.ipynb) - a bare API, to use anywhere
        * [pytorch.ipynb](https://github.com/stared/livelossplot/blob/master/examples/pytorch.ipynb) - a bare API, as applied to PyTorch
        * [pytoune.ipynb](https://github.com/stared/livelossplot/blob/master/examples/pytoune.ipynb) - a PyToune callback ([PyToune](https://pytoune.org/) is a Keras-like framework for PyTorch)
        * [torchbearer.ipynb](https://github.com/stared/livelossplot/blob/master/examples/torchbearer.ipynb) - an example using the built in functionality from torchbearer ([torchbearer](https://github.com/ecs-vlc/torchbearer) is a model fitting library for PyTorch)
        * [neptune-minimal-terminal.py](https://github.com/stared/livelossplot/blob/master/examples/neptune-minimal-terminal.py) - a [Neptune.ML](https://neptune.ml/) Python script (so far the only way to use livelossplot outside of Jupyter)
        * [neptune-minimal-jupyter.ipynb](https://github.com/stared/livelossplot/blob/master/examples/neptune-minimal-jupyter.ipynb) - a [Neptune.ML](https://neptune.ml/) Jupyter Notebook integration
        
        ## Overview
        
        Text logs are easy, but it's easy to miss the most crucial information: is it learning, doing nothing or overfitting?
        
        Visual feedback allows us to keep track of the training process. Now there is one for Jupyter.
        
        If you want to get serious - use [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard) or even better - [Neptune - Machine Learning Lab](https://neptune.ml/) (as it allows to compare between models, in a Kaggle leaderboard style).
        
        But what if you just want to train a small model in Jupyter Notebook? Here is a way to do so, using `livelossplot` as a plug&play component.
        
        It started as [this gist](https://gist.github.com/stared/dfb4dfaf6d9a8501cd1cc8b8cb806d2e). Since it went popular, I decided to rewrite it as a package.
        
        ## To do
        
        * Add docstrings
        * Add [Bokeh](https://bokeh.pydata.org/) backend
        * History saving
        * Add connectors to TensorBoard
        
        If you want more functionality - open an [Issue](https://github.com/stared/livelossplot/issues) or even better - prepare a [Pull Request](https://github.com/stared/livelossplot/pulls).
        
Keywords: keras,pytorch,plot,chart
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
