# livelossplot

**Repository Path**: mirrors_lepy/livelossplot

## Basic Information

- **Project Name**: livelossplot
- **Description**: Live training loss plot in Jupyter Notebook for Keras, PyTorch and others
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-25
- **Last Updated**: 2025-08-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Live Loss Plot

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/).

```
from livelossplot import PlotLossesKeras

model.fit(X_train, Y_train,
          epochs=10,
          validation_data=(X_test, Y_test),
          callbacks=[PlotLossesKeras()],
          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 compability (for exploration and teaching). Simplicity of use.

## Installation

To install [this verson from PyPI](https://pypi.python.org/pypi/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:

* [keras_example.ipynb](https://github.com/stared/livelossplot/blob/master/keras_example.ipynb) - a Keras callback
* [minimal_example.ipynb](https://github.com/stared/livelossplot/blob/master/minimal_example.ipynb) - a bare API, to use anyware
* [pytorch_example.ipynb](https://github.com/stared/livelossplot/blob/master/pytorch_example.ipynb) - a bare API, as applied to PyTorch

## 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 proces. 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 [Bokeh](https://bokeh.pydata.org/) backend
* History saving
* Add connectors to Tensorboard and Neptune

If you want more functionality - open an Issue or even better - prepare a Pull Request.