Base class for applying common real-time data preprocessing.
This class is meant to be used as an argument of
input_data. When training
a model, the defined pre-processing methods will be applied at both
training and testing time. Note that DataAugmentation is similar to
DataPreprocessing, but only applies at training time.
Scale each sample by the specified standard deviation. If no std specified, std is evaluated over all samples data.
float(optional). Provides a custom standard derivation. If none provided, it will be automatically caluclated based on the training dataset. Default: None.
Zero center every sample with specified mean. If not specified, the mean is evaluated over all samples.
float(optional). Provides a custom mean. If none provided, it will be automatically caluclated based on the training dataset. Default: None.
Scale each sample with its standard deviation.
Zero center each sample by subtracting it by its mean.
Apply ZCA Whitening to data.
array(optional). Use the provided pre-computed principal component instead of computing it.