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Visualization of Experiment Hyperparameters in Generic Frameworks

This topic shows you how to set experiment hyperparams and their effects. The topic builds on the script that resulted from steps in Integrating MissingLink with a Generic Network (With Steps).

Note

For each framework that MissingLink supports, there are hyperparameters that will be retrieved automatically.

The steps that are covered are:

  • Define a hyperparam.
  • Set hyperparams to MissingLink's callback.

Preparation

Go through Generic Inegration with Network (With Steps).

Note

Ensure that you can successfully run the full code sample training script that resulted from integration with the MissingLink SDK. In the steps that follow below, the script is further developed to include hyperparams.

Write code

  1. Add a dropout rate hyperparam:

    # Training params
    DROPOUT_RATE = 0.1
    LEARNING_RATE = 0.01
    
  2. Set hyperparams to an experiment:

    In the base script, edit the create_experiment and add the hyperparams.

    with missinglink_project.experiment(
        display_name='MNIST multilayer perception',
        description='Two fully connected hidden layers',
        hyperparams={'dropout_rate': DROPOUT_RATE}) as experiment:
    

You should have added hyperparams to your experiment successfully.

  • Inspect the resulting script here.

  • Run the new script and see how MissingLink's dashboard helps with monitoring the experiment's hyperparams. A description follows.

Viewing the new functionality on the dashboard

You can see the hyperparams across different experiments on your MissingLink dashboard.