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clearml-docs/docs/guides/frameworks/keras/keras_tensorboard.md
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title
Keras with TensorBoard

The keras_tensorboard.py example demonstrates the integration of ClearML into code which uses Keras and TensorBoard.

The example does the following:

  1. Trains a simple deep neural network on the Keras built-in MNIST dataset.

  2. Builds a sequential model using a categorical crossentropy loss objective function.

  3. Specifies accuracy as the metric, and uses two callbacks: a TensorBoard callback and a model checkpoint callback.

  4. During script execution, 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.

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Histograms

Histograms for layer density appear in RESULTS > PLOTS.

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Hyperparameters

ClearML automatically logs command line options generated with argparse, and TensorFlow Definitions.

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

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TensorFlow Definitions appear in TF_DEFINE.

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Console

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

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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.

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