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Source code for pytorch_lightning.metrics.functional.mean_absolute_error

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Tuple

import torch

from pytorch_lightning.metrics.utils import _check_same_shape


def _mean_absolute_error_update(preds: torch.Tensor, target: torch.Tensor) -> Tuple[torch.Tensor, int]:
    _check_same_shape(preds, target)
    sum_abs_error = torch.sum(torch.abs(preds - target))
    n_obs = target.numel()
    return sum_abs_error, n_obs


def _mean_absolute_error_compute(sum_abs_error: torch.Tensor, n_obs: int) -> torch.Tensor:
    return sum_abs_error / n_obs


[docs]def mean_absolute_error(preds: torch.Tensor, target: torch.Tensor) -> torch.Tensor: """ Computes mean absolute error Args: pred: estimated labels target: ground truth labels Return: Tensor with MAE Example: >>> x = torch.tensor([0., 1, 2, 3]) >>> y = torch.tensor([0., 1, 2, 2]) >>> mean_absolute_error(x, y) tensor(0.2500) """ sum_abs_error, n_obs = _mean_absolute_error_update(preds, target) return _mean_absolute_error_compute(sum_abs_error, n_obs)

© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision c462b274.

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