livelossplot's Introduction

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 for Keras, PyTorch and other frameworks. An open source Python package by Piotr Migdał.

from livelossplot import PlotLossesKeras

model.fit(X_train, Y_train,
          epochs=10,
          validation_data=(X_test, Y_test),
          callbacks=[PlotLossesKeras()],
          verbose=0)

So remember, log your loss!

(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, 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 - a Keras callback
minimal_example.ipynb - a bare API, to use anyware
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 or even better - Neptune - Machine Learning Lab (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. Since it went popular, I decided to rewrite it as a package.