Source code for pytorch_lightning.metrics.functional.mean_squared_log_error
# Copyright The PyTorch Lightning team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Tuple
import torch
from pytorch_lightning.metrics.utils import _check_same_shape
def _mean_squared_log_error_update(preds: torch.Tensor, target: torch.Tensor) -> Tuple[torch.Tensor, int]:
_check_same_shape(preds, target)
sum_squared_log_error = torch.sum(torch.pow(torch.log1p(preds) - torch.log1p(target), 2))
n_obs = target.numel()
return sum_squared_log_error, n_obs
def _mean_squared_log_error_compute(sum_squared_log_error: torch.Tensor, n_obs: int) -> torch.Tensor:
return sum_squared_log_error / n_obs
[docs]def mean_squared_log_error(preds: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Computes mean squared log error
Args:
pred: estimated labels
target: ground truth labels
Return:
Tensor with RMSLE
Example:
>>> x = torch.tensor([0., 1, 2, 3])
>>> y = torch.tensor([0., 1, 2, 2])
>>> mean_squared_log_error(x, y)
tensor(0.0207)
"""
sum_squared_log_error, n_obs = _mean_squared_log_error_update(preds, target)
return _mean_squared_log_error_compute(sum_squared_log_error, n_obs)