Creating Experiments

There are two types of experiments: training experiments, which involve training a new or existing model, and testing experiments, which involve testing an existing model.

keras.train --config_file path/to/config/file.yaml

keras.test --config_file path/to/config/file.yaml

Specifying Keras Objects

Keras Declarative automatically discovers objects, such as layers and loss functions, which are available in core TensorFlow, TensorFlow MRI and TF Playground. This means these objects can be used in the configuration file without explicit registration.

Serializable Keras objects, such as layers and loss functions, are specified by a class name and a configuration dictionary. If no parameters should be passed to the object, the configuration dictionary may be omitted.

# class name and parameters
training:
  optimizer:
    class_name: Adam
    config:
      learning_rate: 0.001

# class name only (instantiated with default parameters)
training:
  optimizer: Adam

External Modules

It is possible to use objects defined in external modules within Keras Declarative, i.e., objects that are not part of either core TensorFlow, TensorFlow MRI or TF Playground. This is particularly useful to define preprocessing functions but can be used for any other purpose. External modules can be specified with the $external directive.

Any external modules used during an experiment will be automatically saved to the experiment folder to enable reproducibility.

data:
  transforms:
    train:
      - type: map
        map:
          map_func:
            $external:
              # Use a preprocessing function defined in an external module.
              filename: /path/to/preprocessing_fn.py
              object_name: preprocessing_fn
              # Any parameters that should be passed to this object may be
              # specified here.
              args: null
              kwargs: null

Anchors and Aliases

If you need to repeat a node more than once, you can anchor it once with the & character and then alias it any number of times using the * character. For example, to use the same list of data transforms for the training and validation sets, you may use:

data:
  transforms:
    train: &transforms
      # Define the list of transforms here.

    val: *transforms  # Reusing training transforms here.

Hyperparameter Tuning

Keras Declarative can configure the Keras Tuner to automatically tune one or more parameters.

Most parameters can be set as tunable using the $tunable directive. For example, to tune the kernel size of a U-Net model, you might use:

model:
  network:
    class_name: UNet
    config:
      scales: 3
      base_filters: 32
      kernel_size:
        $tunable:
          type: int
          int:
            name: kernel_size
            min_value: 3
            max_value: 7
            step: 2

Valid tunable types are boolean, choice, fixed, float and int. For more details, see https://keras.io/api/keras_tuner/hyperparameters/.

The tuner type and options can be specified with tuning.tuner parameter:

tuning:
  tuner:
    class_name: Hyperband
    config:
      objective:
        name: val_ssim
        direction: max
      max_epochs: 100

Available tuners are RandomSearch, BayesianOptimization and Hyperband. For more details about these tuners and their options, see https://keras.io/api/keras_tuner/tuners/.

Note that some parameters cannot be tuned. These include all parameters under experiment and under data.sources.