autograd_backward | R Documentation |

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).

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

`tensors` |
(list of Tensor) – Tensors of which the derivative will be computed. |

`grad_tensors` |
(list of (Tensor or |

`retain_graph` |
(bool, optional) – If |

`create_graph` |
(bool, optional) – If |

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

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)) }

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