torch: autograd_backward – R documentation

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autograd_backward

Computes the sum of gradients of given tensors w.r.t. graph leaves.

Description

The graph is differentiated using the chain rule. If any of tensors are non-scalar (i.e. their data has more than one element) and require gradient, then the Jacobian-vector product would be computed, in this case the function additionally requires specifying grad_tensors. It should be a sequence of matching length, that contains the “vector” in the Jacobian-vector product, usually the gradient of the differentiated function w.r.t. corresponding tensors (None is an acceptable value for all tensors that don’t need gradient tensors).

Usage

autograd_backward(
  tensors,
  grad_tensors = NULL,
  retain_graph = create_graph,
  create_graph = FALSE
)

Arguments

`tensors`	(list of Tensor) – Tensors of which the derivative will be computed.
`grad_tensors`	(list of (Tensor or `NULL)) – The “vector” in the Jacobian-vector product, usually gradients w.r.t. each element of corresponding tensors.` NULL`values can be specified for scalar Tensors or ones that don’t require grad. If a`NULL' value would be acceptable for all grad_tensors, then this argument is optional.
`retain_graph`	(bool, optional) – If `FALSE`, the graph used to compute the grad will be freed. Note that in nearly all cases setting this option to `TRUE` is not needed and often can be worked around in a much more efficient way. Defaults to the value of `create_graph`.
`create_graph`	(bool, optional) – If `TRUE`, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to `FALSE`.

Details

This function accumulates gradients in the leaves - you might need to zero them before calling it.

Examples

if (torch_is_installed()) {
x <- torch_tensor(1, requires_grad = TRUE)
y <- 2 * x

a <- torch_tensor(1, requires_grad = TRUE)
b <- 3 * a

autograd_backward(list(y, b))

}

torch

Tensors and Neural Networks with 'GPU' Acceleration

v0.3.0

MIT + file LICENSE

Authors

Daniel Falbel [aut, cre, cph], Javier Luraschi [aut], Dmitriy Selivanov [ctb], Athos Damiani [ctb], Christophe Regouby [ctb], Krzysztof Joachimiak [ctb], RStudio [cph]

Initial release