torch.nn.functional.huber_loss# torch.nn.functional.huber_loss(input, target, reduction='mean', delta=1.0, weight=None)[source]# Compute the Huber loss, with optional weighting. Function uses a squared term if the absolute element-wise error falls below delta and a delta-scaled L1 term otherwise. When delta equals 1, this loss is equivalent to SmoothL1Loss. In general, Huber loss differs from SmoothL1Loss by a factor of delta (AKA beta in Smooth L1). See HuberLoss for details. Parameters input (Tensor) – Predicted values. target (Tensor) – Ground truth values. reduction (str, optional) – Specifies the reduction to apply to the output: ‘none’ | ‘mean’ | ‘sum’. ‘mean’: the mean of the output is taken. ‘sum’: the output will be summed. ‘none’: no reduction will be applied. Default: ‘mean’. delta (float, optional) – The threshold at which to change between delta-scaled L1 and L2 loss. Default: 1.0. weight (Tensor, optional) – Weights for each sample. Default: None. Returns Huber loss (optionally weighted). Return type Tensor