trainplot 0.1.1

Dynamically updating plots in Jupyter notebooks, e.g. for visualizing training progress.

trainplot

Dynamically updating plots in Jupyter notebooks, e.g. for visualizing training progress. Inspired by livelossplot.

Installation

pip install trainplot

Usage

In your Jupyter notebook import it using:

from trainplot import TrainPlot

Then you can use it like this:

trainplot = TrainPlot()

for i in range(100):
    trainplot(i = i+random.random()*10, root = i**.5*3)
    time.sleep(0.1)

trainplot.close()

---

It also works with tqdm and printing:

trainplot = TrainPlot()

for i in trange(50):
    trainplot(i=i, root=i**.5)
    if i % 10 == 0:
        print(f'currently at {i} iterations')
    time.sleep(0.1)

trainplot.close()

---

You can also add a bunch of custumizations, e.g.:

trainplot = TrainPlot(
    update_period=.2,
    fig_args=dict(nrows=2, ncols=2, figsize=(10, 8), gridspec_kw={'height_ratios': [1, 1], 'width_ratios': [1, 1]}),
    plot_pos={'loss': (0, 0, 0), 'accuracy': (0, 1, 0), 'val_loss': (1, 0, 0), 'val_accuracy': (1, 1, 0)},
    plot_args={'loss': {'color': 'orange'}, 'accuracy': {'color': 'green'}, 'val_loss': {'color': 'orange', 'label': 'validation loss'}, 'val_accuracy': {'color': 'green', 'label': 'validation accuracy'}},
)

for i in range(100, 200):
    trainplot(step=i, loss=(i/100-2)**4, accuracy=i/2, val_loss=(i/100-2.1)**4, val_accuracy=i/2.1)
    time.sleep(0.1)

trainplot.close()