Keras with TensorBoard

Trains is now ClearML

This documentation applies to the legacy Trains versions. For the latest documentation, see ClearML.

The keras_tensorboard.py example demonstrates the integration of Trains into code which uses Keras and TensorBoard. It trains a simple deep neural network on the Keras built-in MNIST dataset. It builds a sequential model using a categorical crossentropy loss objective function, specifies accuracy as the metric, and uses two callbacks: a TensorBoard callback and a model checkpoint callback. When the script runs, it creates an experiment named Keras with TensorBoard example which is associated with the examples project.

Scalars

The loss and accuracy metric scalar plots appear in the RESULTS > SCALARS, along with the resource utilization plots, which are titled :monitor: machine.

Histograms

Histograms for layer density appear in RESULTS > PLOTS.

Hyperparameters

Trains automatically logs command line options when you use argparse, and TensorFlow DEFINEs.

Command line options appear in CONFIGURATIONS > HYPER PARAMETERS > Args.

TensorFlow DEFINEs appear in TF_DEFINE.

Log

Text printed to the console for training progress, as well as all other console output, appear in RESULTS > LOG.

Configuration objects

In the experiment code, a configuration dictionary is connected to the Task by calling the Task.connect method.

task.connect_configuration({'test': 1337, 'nested': {'key': 'value', 'number': 1}})

It appears in CONFIGURATIONS > CONFIGURATION OBJECTS.