Live Loss Plot
Don't train deep learning models blindfolded! Be impatient and look at each epoch of your training!
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 compatibility (for exploration and teaching). Simplicity of use.
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
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
- pytoune.ipynb - a PyToune callback (PyToune is a Keras-like framework for PyTorch)
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.
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.
- Add Bokeh backend
- History saving
- Add connectors to TensorBoard and Neptune