livelossplot 0.4.2

Live training loss plot in Jupyter Notebook for Keras, PyTorch and others.

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, and others. Open for collaboration! (Some tasks are as simple as writing code docstrings, so - no excuses! :))

This project supported by: Jacek Migdał, Marek Cichy. Join the sponsors - show your ❤️ and support, and appear on the list! It will give me time and energy to work on this project.

from livelossplot.keras import PlotLossesCallback

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

So remember, log your loss!

• (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, 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.ipynb - a Keras callback
• minimal.ipynb - a bare API, to use anywhere
• pytorch.ipynb - a bare API, as applied to PyTorch
• 2d_prediction_maps.ipynb - example of custom plots - 2d prediction maps (0.4.1+)
• poutyne.ipynb - a Poutyne callback (Poutyne is a Keras-like framework for PyTorch)
• torchbearer.ipynb - an example using the built in functionality from torchbearer (torchbearer is a model fitting library for PyTorch)
• neptune-minimal-terminal.py - a Neptune.ML Python script (so far the only way to use livelossplot outside of Jupyter)
• neptune-minimal-jupyter.ipynb - a 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 or even better - Neptune - Machine Learning Lab (as it allows to compare between models, in a Kaggle leaderboard style). Or, well use tensorboard_dir="./logs" or target='neptune'. Now these are included as well!

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.

Oh, an I am in general interested in data vis, see Simple diagrams of convoluted neural networks (and overview of deep learning architecture diagrams):

A good diagram is worth a thousand equations — let’s create more of these!

...or my other data vis projects.

To do

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