API Reference: Training

masterful.training.TrainingReport

class masterful.training.TrainingReport(validation_results=None, gpu_info=None, history=None, model=None)

Structure which holds the results of a training run.

Parameters

  • validation_results (Dict[str, float]) – The final results from evaluating the model on the validation set.

  • gpu_info (Sequence[masterful.utils.gpu.GpuInfo]) – A list of GpuInfo objects, with relevant gpu usage information.

  • history (keras.callbacks.History) – The full training history report, containing the results at the end of each epoch for key metrics.

  • model (Optional[keras.engine.training.Model]) – A reference to the trained model. This will be different than the model passed in for training if model_ensemble is greater than 1.

Return type

None

masterful.training.train

masterful.training.train(*args, **kwargs)

Trains a model using the Masterful platform.

The model passed into this function will be trained against the passed in datasets using the given parameters for regularization, optimization, and semi-supervised learning.

Parameters

  • model – The model to train.

  • model_params – Parameters of the model to train.

  • optimizer_params – Parameters to use for optimization. These can be created directly, or found automatically using masterful.optimization.learn_optimization_params().

  • regularization_params – Parameters to use for regularization. These can be created directly, or found automatically using masterful.regularization.learn_regularization_params().

  • ssl_params – Parameters to use for semi-supervised training. These can be created directly, or learned automatically using masterful.ssl.learn_ssl_params().

  • training_dataset – The labeled dataset to use during training.

  • training_dataset_params – The parameters of the labeled dataset.

  • validation_dataset – An optional validation dataset to use during training. If no validation set is specified, Masterful will autmoatically create one from the labeled dataset.

  • validation_dataset_params – Optional parameters of the validation dataset.

  • unlabeled_datasets – Optional sequence of unlabled datasets and their parameters, to use during training. If an unlabeled dataset is specified, then a set of algorithms must be specified in ssl_params otherwise this will have no effect.

  • synthetic_datasets – Optional sequence of synthetic data and parameters to use during training. The amount of synthetic data used during training is controlled by masterful.regularization.RegularizationParams.synthetic_proportion.

Returns

An instance of TrainingReport with the full results of training the model with the given parameters.