com.intel.analytics.zoo.pipeline.estimator
Estimator
Related Docs:
object Estimator
| package estimator
com.intel.analytics.zoo.pipeline.estimator
Clear gradient clipping parameters.
Clear gradient clipping parameters. In this case, gradient clipping will not be applied. In order to take effect, it needs to be called before fit.
Evaluate the model on the validationSet with the validationMethods.
Evaluate the model on the validationSet with the validationMethods.
validation FeatureSet
validation methods
validation results
Set constant gradient clipping during the training process.
Set constant gradient clipping during the training process. In order to take effect, it needs to be called before fit.
The minimum value to clip by. Double.
The maximum value to clip by. Double.
Clip gradient to a maximum L2-Norm during the training process.
Clip gradient to a maximum L2-Norm during the training process. In order to take effect, it needs to be called before fit.
Gradient L2-Norm threshold. Double.
Train model with provided trainSet and criterion.
Train model with provided trainSet and criterion. The training will end until the endTrigger is triggered. During the training, if checkPointTrigger is defined and triggered, the model will be saved to modelDir. And if validationSet and validationMethod is defined, the model will be evaluated at the checkpoint.
training FeatureSet
Loss function
When to finish the training
When to save a checkpoint and evaluate model.
Validation FeatureSet.
Validation Methods.
self
Estimator class for training and evaluation BigDL models.
Estimator wraps a model, and provide an uniform training, evaluation or prediction operation on both local host and distributed spark environment.
tensor numeric type