作用:衡量input X 和target y之间的差异,用mean squared error (squared L2 norm)计算,其中X和y的形状要相同 from torch.nn import MSELoss inputs = torch.tensor([1,2,3],dtype=torch.float32) targets =torch.tensor([1,2,5],dtype=torch.float32) loss = MSELoss() result = loss(inputs,targets...
class MSELoss(_Loss): r"""Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input :math:`x` and target :math:`y`. The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can be described as: .. math:: \ell(...
1.1nn.MSELoss nn.MSELoss:均方误差(Mean squared error,MSE)损失函数,或者称为平方 L2(squared L2 norm)损失函数 等价函数形式:F.mse_loss() A. 计算公式 对于每个样本的损失函数(或称为误差)为: ln≜l(xn,yn)=(xn−yn)2ln≜l(xn,yn)=(xn−yn)2 其中,xnxn和ynyn分别表示第nn个样本的输出值...
Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input x and target y . 计算输入和目标之间每个元素的均方误差(平方 L2 范数)。 参数 size_average (bool, 可选): 已弃用。请参阅 reduction 参数。 默认情况下,损失在批次中的每个损失元素上取...
MAE是指平均绝对误差,也称L1损失: loss =nn.L1Loss() input= torch.randn(1, 2, requires_grad=True) target= torch.randn(1, 2) output= loss(input, target) 2:torch.nn.MSELoss measures the mean squared error (squared L2 norm) between each element in the inputx and targety. ...
p30:1.均方差(Mean Squared Error,MSE): (1)注意区分MSE和L2范数: L2范数要开根号,而MSE不需要开根号。 用torch.norm函数求MSE的时候不要忘记加上pow(2)。 求导: pytorch实现自动求导:第一种方法:torch.autograd.grad() 设置w需要求导有两种方法: ...
squared_l2,负平方L2距离。 自定义比较器需要实现torchbiggraph.model.AbstractComparator 子类并且在torchbiggraph.model.register_comparator_as()装饰器中注册,指定一个新名称,然后在配置中使用该名称来选择比较器。上述所有操作都可以在配置文件内部完成。
# We are considering the L2-norm loss as our loss function (regression problem), but divided by 2.# Moreover, we further divide it by the number of observations to take the mean of the L2-norm.loss = np.sum(de...
下面定义L2范数惩罚项。这里只惩罚模型的权重参数。 defl2_penalty(w):return(w**2).sum()/2 3.12.3.3 定义训练和测试 下面定义如何在训练数据集和测试数据集上分别训练和测试模型。 batch_size,num_epochs,lr=1,100,0.003net,loss=d2l.linreg,d2l.squared_loss ...
2. Mean Squared Error (L2 Loss Function)torch.nn.MSELoss MSE是最常见的损失函数之一。计算的是...