Keras with TensorBoard¶
The keras_tensorboard.py example demonstrates the integration of ClearML 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
The loss and accuracy metric scalar plots appear in the RESULTS > SCALARS, along with the resource utilization plots, which are titled :monitor: machine.
Histograms for layer density appear in RESULTS > PLOTS.
ClearML automatically logs command line options when you use
argparse, and TensorFlow Definitions.
Command line options appear in CONFIGURATIONS > HYPER PARAMETERS > Args.
TensorFlow Definitions appear in TF_DEFINE.
Text printed to the console for training progress, as well as all other console output, appear in RESULTS > LOG.