torch.nn.MSELoss(*size_average=None*, *reduce=None*, *reduction='mean'*) torch.nn.functional.mse_loss(*input*, *target*, *size_average=None*, *reduce=None*, *reduction='mean'*) Smooth L1 Loss Smooth L1 损失函数通过β结合了MSE 和 MAE 的优点,来自 Fast R-CNN 论文。 当真实值和预测...
self).__init__()self.fc1 = nn.Linear(10, 5)self.fc2 = nn.Linear(5, 1)def forward(self, x):x = nn.functional.relu(self.fc1(x))x = self.fc2(x)return x# 创建模型实例、损失函数和优化器net = Net()criterion = nn.MSELoss()optimizer = optim...
torch.nn.functional.mse_loss(input,target,size_average=None,reduce=None,reduction=mean) → Tensor 参数 size_average: 默认为True, 计算一个batch中所有loss的均值;reduce为 False时,忽略这个参数; reduce: 默认为True, 计算一个batch中所有loss的均值或者和; reduce = False,size_average 参数失效,返回的 l...
import torch# input和target分别为MESLoss的两个输入input = torch.tensor([0.,0.,0.])target = torch.tensor([1.,2.,3.])# MSELoss函数的具体使用方法如下所示,其中MSELoss函数的参数均为默认参数。loss = torch.nn.MSELoss(size_average=None, reduce=None, reduction='mean')loss = loss(input, t...
output = loss(input, target) output.backward() 2 均方误差损失 MSELoss 用于测量输入 x 和目标 y 中每个元素之间的均方误差(平方 L2 范数)。 图1 主要参数: reduction 参数值mean和sum,mean:返回loss和的平均值;sum:返回loss的和。默认:mean。
F.cross_entropy(input, target)：计算交叉熵损失。F.mse_loss(input, target)：计算均方误差 (Mean Squared Error, MSE) 损失。卷积操作：F.conv2d(input, weight)：应用二维卷积。F.conv1d(input, weight)：应用一维卷积。池化操作：F.max_pool2d(input, kernel_size)：应用二维最大池化。F.avg_pool2d(...
loss = torch.nn.MSELoss(reduction='sum') loss = loss(X, Y) print(loss) loss.backward() print(X.grad) 1. 2. 3. 4. 5. 则 例如 代码实现 import torch X = torch.tensor([[3, 1], [4, 2], [5, 3]], dtype=torch.float, requires_grad=True) ...
torch.nn.MSELoss() torch.nn.MSELoss()是PyTorch中用来计算均方误差(Mean Squared Error,简称MSE)的损失函数。它可以用于回归问题中,衡量模型预测值与真实值之间的差距。 数学原理: 均方误差是指每个样本的预测值与真实值之间差的平方的均值。对于一个有n个样本的数据集,MSE可以表示为:...
The following are 30 code examples of torch.nn.functional.mse_loss(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available ...
x,w)+b# 计算二元交叉熵损失，并应用sigmoid函数，使用logits形式的z和目标yloss=torch.nn.functional...