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Class Mappings for TensorFlow

This topic shows you how to set class mappings and their effects. The topic builds on Getting Started for TensorFlow with steps.

The following steps are covered:

  • Create a class mapping for MNIST.
  • Set the class mapping to MissingLink's callback.

Preparation

Go through Getting Started for TensorFlow with steps.

Note

Ensure that you can successfully run the mnist.py training script that resulted from integration with the MissingLink SDK. In the steps that follow below, the script is further developed to include class mappings.

Write code

  1. Create a class mapping for MNIST.

    Right above the original setting of display name and description, add a mnist class mapping:

    mnist_class_mapping = {
        0: 'zero',
        1: 'one',
        2: 'two',
        3: 'three',
        4: 'four',
        5: 'five',
        6: 'six',
        7: 'seven',
        8: 'eight',
        9: 'nine',
    }
    
  2. Set the class mapping to the callback.

    In the base script, the display name and description of an experiment has already been defined. Add an extra line right below to set the class mappings:

    with missinglink_project.create_experiment(
        display_name='MNIST multilayer perception',
        description='Two fully connected hidden layers',
        class_mapping=mnist_class_mapping) as experiment:
    

You should have added class mappings 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 class mappings.