这是因为torch.nn.MSELoss()默认返回的是每个样本的MSE值之和,并且在计算总体损失时通常会将其除以样本数量来得到平均损失。 在代码中,loss = criterion(y_pred.squeeze(), Y_train.squeeze())语句计算了y_pred和Y_train之间的MSE损失,然后通过调用item()方法获取了该批次训练样本的平均MSE损失。如果希望获取该...
size_average(bool):当reduce=True时有效。为True时,返回的loss为平均值;为False时,返回的各样本的loss之和。 reduce(bool):返回值是否为标量,默认为True。 (5)SmoothL1Loss CLASS torch.nn.SmoothL1Loss(size_average=None, reduce=None, reduction=‘mean’, beta=1.0) 功能: 计算...
CATCH_RETURN_Tensor( autoopts =torch::nn::functional::MSELossFuncOptions(); ApplyReduction(opts, reduction); res =ResultTensor(torch::nn::functional::mse_loss(*input, *target, opts)); ) } If this is indeed incorrect, I would be happy to submit a fix in a PR, but am still figuring...
criterion = nn.MSECriterion() criterion.sizeAverage = false ## MultiCriterion ## criterion = nn.MultiCriterion() This returns a Criterion which is a weighted sum of other Criterion. Criterions are added using the method: criterion:add(singleCriterion, weight) where weight is a scalar. ##...
Cannot retrieve contributors at this time executable file624 lines (447 sloc)21.1 KB RawBlame Criterions Criterionsare helpful to train a neural network. Given an input and a target, they compute a gradient according to a given loss function....