Keras with TensorBoard - Jupyter Notebook
Trains is now ClearML
This documentation applies to the legacy Trains versions. For the latest documentation, see ClearML.
The Allegro_Trains_keras_TB_example.ipynb example demonstrates Trains in automatic logging of code running in Jupyter Notebook that 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
Colab notebooks project.
Open in Google Colab
trains GitHub repository, this example includes a clickable icon to open the notebook in Google Colab.
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.
Trains automatically logs TensorFlow DEFINEs which appear in CONFIGURATIONS > HYPER PARAMETERS > TF_DEFINE.
Text printed to the console for training progress, as well as all other console output, appear in RESULTS > LOG.
The configuration appears in CONFIGURATIONS > CONFIGURATION OBJECTS > General.