nn.L2Loss()

nn.L2Loss() is a loss function available in PyTorch. It is used to calculate the mean square error between the predicted and target values. It is commonly used in regression problems where the goal is to predict a continuous variable. The L2Loss() function calculates the difference between the predicted value and the target value, squares it and returns the mean of all the squared differences. This loss function is generally used for training neural networks using stochastic gradient descent or any other optimization algorithm. It is a popular loss function because it is differentiable, which means it can be used for backpropagation.

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nn.L2loss和nn.MSELoss

nn.L2loss和nn.MSELoss是用于计算平方误差损失的两个PyTorch中的类。它们的作用是根据给定的目标值和预测值计算平方误差。nn.L2loss和nn.MSELoss的功能相同，都是计算L2损失或均方误差(MSE)损失。根据PyTorch官方文档的说明，它们可以使用相同的方式来计算损失。 <span class="em">1</span><span class="em">2</span><span class="em">3</span>
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torch.nn.L2Loss()

`torch.nn.L2Loss()` 是 PyTorch 中用于计算 L2 损失（也称为欧几里得距离）的函数。它用于计算预测值和真实值之间的平方差，并返回这个平方差的平均值。它的公式如下：

L2 loss = (1/n) * ∑(y - y_pred)^2

其中，y 是真实值，y_pred 是预测值，n 是样本数。

在训练神经网络时，通常使用 L2 loss 作为损失函数，用于度量模型的预测值和真实值之间的差距，从而优化模型的参数。