Keras + TensorFlow Realtime training chart

Question

I have the following code running inside a Jupyter notebook:

# Visualize training history
from keras.models import Sequential
from keras.layers import Dense
impo         


        

Answer 1

Keras comes with a callback for TensorBoard.

You can easily add this behaviour to your model and then just run tensorboard on top of the logging data.

callbacks = [TensorBoard(log_dir='./logs')]
result = model.fit(X, Y, ..., callbacks=callbacks)

And then on your shell:

tensorboard --logdir=/logs

If you need it in your notebook, you can also write your own callback to get metrics while training:

 class LogCallback(Callback):

    def on_epoch_end(self, epoch, logs=None):
        print(logs["train_accuracy"])

This would get the training accuracy at the end of the current epoch and print it. There's some good documentation around it on the official keras site.

Answer 2

There is livelossplot Python package for live training loss plots in Jupyter Notebook for Keras (disclaimer: I am the author).

from livelossplot import PlotLossesKeras

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

To see how does it work, look at its source, especially this file: https://github.com/stared/livelossplot/blob/master/livelossplot/outputs/matplotlib_plot.py (from IPython.display import clear_output and clear_output(wait=True)).

A fair disclaimer: it does interfere with Keras output.