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How it looks

live loss and accuracy.gif

Introduction

Live training loss plot in Jupyter Notebook for Keras, PyTorch and other frameworks. An open-source Python package by Piotr Migda?, Bart?omiej Olechno and others. Open for collaboration! (Some tasks are as simple as writing code docstrings, so - no excuses! :))

Installation

# 打開虛擬環(huán)境
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

PlotLosses for a generic API.

from livelossplot import PlotLosses
from time import sleep
...
plotlosses = PlotLosses()
# begin loop:
  plotlosses.update({'acc': 0.7, 'val_acc': 0.4, 'loss': 0.9, 'val_loss': 1.1})
  plotlosses.send()  # draw, update logs, etc
  sleep(.01)
# end loop

More Examples

Look at notebook files with full working examples:

• keras.ipynb - a Keras callback
• minimal.ipynb - a bare API, to use anywhere
• bokeh.ipynb - a bare API, plots with Bokeh (open it in Colab to see the plots)
• 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.py and neptune.ipynb - a Neptune.AI
• matplotlib.ipynb - a Matplotlib output example
• various_options.ipynb - an extended API for metrics grouping and custom outputs

You run examples in Colab.

Concrete Example: Matplotlib output

%matplotlib inline

from time import sleep
from matplotlib import pyplot as plt
import numpy as np

from livelossplot import PlotLosses
from livelossplot.outputs import MatplotlibPlot

def test_output(outputs):
    groups = {'acccuracy': ['acc', 'val_acc'], 'log-loss': ['loss', 'val_loss']}
    plotlosses = PlotLosses(groups=groups, outputs=outputs)
    
    for i in range(100):
        plotlosses.update({
            'acc': 1 - np.random.rand() / (i + 2.),
            'val_acc': 1 - np.random.rand() / (i + 0.5),
            'loss': 1. / (i + 2.),
            'val_loss': 1. / (i + 0.5)
        })
        plotlosses.send()
        sleep(.01)

outputs = [MatplotlibPlot()]
test_output(outputs)

Custom after subplot function

You can replace after subplot function, which operates on suitable group axis.

def custom_after_subplot(ax: plt.Axes, group: str, x_label: str):
    """Make logarithmic scale on loss chart"""
    if group == 'log-loss':
        ax.loglog()
    ax.set_xlabel(x_label)
   
outputs = [MatplotlibPlot(after_subplot=custom_after_subplot)]
test_output(outputs)