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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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