trainplot 0.2.0

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, and aims to be easier to use with better jupyter notebook support.

Trainplot outputs the matplotlib figure to an ipywidgets.Output widget, so it doesn't interfere with other outputs like tqdm or print statements. To avoid wasting resources and flickering, the figure is only updated with a given update_period.

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()

---

There is a tf/keras callback:

from trainplot import TrainPlotKeras

...

model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=10, callbacks=[TrainPlotKeras()])

---

It also works together with e.g. tqdm.notebook 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()

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Check the examples folder for example notebooks.