TensorBoard - Plot training and validation losses on the same graph?

Question

Is there a way to plot both the training losses and validation losses on the same graph?

It\'s easy to have two separate scalar summaries for each of them i

Answer 1

The work-around I have been doing is to use two SummaryWriter with different log dir for training set and cross-validation set respectively. And you will see something like this:

Answer 2

Tensorboard is really nice tool but by its declarative nature can make it difficult to get it to do exactly what you want.

I recommend you checkout Losswise (https://losswise.com) for plotting and keeping track of loss functions as an alternative to Tensorboard. With Losswise you specify exactly what should be graphed together:

import losswise

losswise.set_api_key("project api key")
session = losswise.Session(tag='my_special_lstm', max_iter=10)
loss_graph = session.graph('loss', kind='min')

# train an iteration of your model...
loss_graph.append(x, {'train_loss': train_loss, 'validation_loss': validation_loss})
# keep training model...

session.done()

And then you get something that looks like:

Notice how the data is fed to a particular graph explicitly via the loss_graph.append call, the data for which then appears in your project's dashboard.

In addition, for the above example Losswise would automatically generate a table with columns for min(training_loss) and min(validation_loss) so you can easily compare summary statistics across your experiments. Very useful for comparing results across a large number of experiments.