loss {gnn} | R Documentation |
Implementation of various loss functions to measure statistical discrepancy between two datasets.
loss(x, y, type = c("MMD", "CvM", "MSE", "BCE"), ...) MMD(x, y, ...) CvM(x, y)
x |
2d-tensor or (n, d)-matrix (during training, n is the batch size and d is the dimension of the input dataset). |
y |
2d-tensor or (m, d)-matrix (during training, m is the batch size (and typically equal to n) and d is the dimension of the input dataset). |
type |
|
... |
additional arguments passed to the underlying functions,
most notably |
loss()
returns a 0d tensor containing the loss.
MMD()
and CvM()
return a 0d tensor (if x
and y
are tensors) or numeric(1)
(if x
or
y
are R matrices).
Marius Hofert and Avinash Prasad
Kingma, D. P. and Welling, M. (2014). Stochastic gradient VB and the variational auto-encoder. Second International Conference on Learning Representations (ICLR). See https://keras.rstudio.com/articles/examples/variational_autoencoder.html
RĂ©millard, B. and Scaillet, O. (2009). Testing for equality between two copulas. Journal of Multivariate Analysis 100, 377–386.
FNN()
where loss()
is used.