AdditiveNoiseAugmentation¶
Add univariate noise drawn from a given distribution to the data.
This simulates uniform random noise that applies the same to every channel, sample, instance, and so forth. A reasonable starting point is a normal distribution with a standard deviation of 25uV, but check the unit of your data to be sure that it is matched appropriately. As a special case, if our data is standardized or whitened, the noise values should be divided by at least 100. The optimal noise likely depends to a large extent on the nature and amount of available training data, so be prepared to experiment with a range of at least 10-50uV. Like most augmentation nodes, this node does not by itself amplify the amount of data, which therefore has to be done beforehand using, for example, the RepeatAlongAxis node. Also as with most augmentation nodes, you need to wire in a random seed (for example using the DrawRandomSeed node, see docs for more info) to ensure reproducibility. You also need to wire a distribution to the dist input to specify the distribution of interest (e.g., NormalDistribution). Version 0.8.1
Ports/Properties¶
data¶
Data to process.
seed¶
Random seed for deterministic results.
dist¶
Distribution to use.
is_training¶
Whether the node is used in training mode.
bypass¶
Whether to bypass the augmentation and pass the input data through unchanged.
set_breakpoint¶
Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.
metadata¶
User-definable meta-data associated with the node. Usually reserved for technical purposes.