deeplearning/modulus/modulus-core/_modules/modulus/metrics/general/mse.html

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Source code for modulus.metrics.general.mse

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# TODO(Dallas) Introduce Ensemble RMSE and MSE routines.

from typing import Union

import torch

Tensor = torch.Tensor


[docs]def mse(pred: Tensor, target: Tensor, dim: int = None) -> Union[Tensor, float]: """Calculates Mean Squared error between two tensors Parameters ---------- pred : Tensor Input prediction tensor target : Tensor Target tensor dim : int, optional Reduction dimension. When None the losses are averaged or summed over all observations, by default None Returns ------- Union[Tensor, float] Mean squared error value(s) """ return torch.mean((pred - target) ** 2, dim=dim)
[docs]def rmse(pred: Tensor, target: Tensor, dim: int = None) -> Union[Tensor, float]: """Calculates Root mean Squared error between two tensors Parameters ---------- pred : Tensor Input prediction tensor target : Tensor Target tensor dim : int, optional Reduction dimension. When None the losses are averaged or summed over all observations, by default None Returns ------- Union[Tensor, float] Root mean squared error value(s) """ return torch.sqrt(mse(pred, target, dim=dim))
© Copyright 2023, NVIDIA Modulus Team. Last updated on Apr 19, 2024.