def __init__(self): super(HasMetrics, self).__init__() self.metrics = Param(self, "metrics", "Keras metrics")
def __init__(self): super(HasNumberOfWorkers, self).__init__() self.num_workers = Param(self, "num_workers", "number of workers") self._setDefault(num_workers=8)
def __init__(self): super(HasKerasOptimizerConfig, self).__init__() self.optimizer_config = Param(self, "optimizer_config", "Serialized Keras optimizer properties") self._setDefault(optimizer_config=None)
def __init__(self): super(HasValidationSplit, self).__init__() self.validation_split = Param(self, "validation_split", "validation split percentage") self._setDefault(validation_split=0.1)
def __init__(self): super(HasVerbosity, self).__init__() self.verbose = Param(self, "verbose", "Stdout verbosity") self._setDefault(verbose=0)
def __init__(self): super(HasBatchSize, self).__init__() self.batch_size = Param(self, "batch_size", "Batch size") self._setDefault(batch_size=32)
def __init__(self): super(HasEpochs, self).__init__() self.epochs = Param(self, "epochs", "Number of epochs to train") self._setDefault(epochs=10)
def __init__(self, inputCol=None, outputCol=None, alpha=None): super(BoxCoxTransformer, self).__init__() self.alpha = Param(self, "alpha", 0) self._setDefault(alpha=0) kwargs = self.__init__._input_kwargs self.setParams(**kwargs)