pytorch实现maeloss

pytorch中没有内置的MAELoss函数。但可以通过自定义损失函数来实现MAE损失。下面是一个示例代码，展示了如何计算MAE损失。

import torch
import torch.nn as nn

class MAELoss(nn.Module):
    def __init__(self):
        super(MAELoss, self).__init__()

    def forward(self, input, target):
        return torch.abs(input - target).mean()

# 使用示例
loss_fn = MAELoss()
input = torch.randn(3, 5, requires_grad=True)
target = torch.randn(3, 5)
loss = loss_fn(input, target)
print("MAE Loss: ", loss)

相关问题

pytorch MAELoss

PyTorch does not provide a built-in implementation of the Mean Absolute Error (MAE) loss function. However, you can easily define it using PyTorch operations. Here's an example implementation:

import torch

class MAELoss(torch.nn.Module):
    def __init__(self):
        super(MAELoss, self).__init__()

    def forward(self, predictions, targets):
        return torch.mean(torch.abs(predictions - targets))

You can use this custom MAELoss class in your PyTorch models by instantiating an object of it and calling it as a function, passing in your predictions and target tensors.

Here's an example usage:

loss_function = MAELoss()
predictions = torch.tensor([1.0, 2.0, 3.0])
targets = torch.tensor([2.0, 2.5, 3.5])
loss = loss_function(predictions, targets)
print(loss.item())  # Print the loss value

This will calculate the MAE loss between the predictions and targets tensors.