![]() ![]() Of computation all at once, thereby saving many sequential kernel calls. Implementations, which combine parameters into a multi-tensor and run the big chunks ![]() For-looping is usually slower than our foreach The most straightforward implementations are for-loops over the parameters withīig chunks of computation. We have 3 major categories of implementations: for-loop, foreach (multi-tensor), andįused. Implementation for the current device if no particular implementation has been Readability and/or generality, so we attempt to default to the generally fastest Many of our algorithms have various implementations optimized for performance, Implements the resilient backpropagation algorithm. Implements L-BFGS algorithm, heavily inspired by minFunc. Implements Averaged Stochastic Gradient Descent. ![]() Implements Adamax algorithm (a variant of Adam based on infinity norm). Implements lazy version of Adam algorithm suitable for sparse tensors. Sets the gradients of all optimized torch.Tensor s to zero. Performs a single optimization step (parameter update). Returns the state of the optimizer as a dict. Options (used when a parameter group doesn’t specify them).Īdd a param group to the Optimizer s param_groups. Specifies what Tensors should be optimized.ĭefaults – (dict): a dict containing default values of optimization Params ( iterable) – an iterable of torch.Tensor s orĭict s. Satisfy those properties are sets and iterators over values of dictionaries. Ordering that is consistent between runs. Parameters need to be specified as collections that have a deterministic
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