avg_factor (int, optional): Average factor that is used to average the loss. Defaults to None. Returns: torch.Tensor: The calculated loss """ loss = self.loss_weight * mse_loss( pred, target, weight, reduction=self.reduction, avg_factor=avg_factor) return loss ...
paddle.nn.functional.nll_loss({}) """ ) code = API_TEMPLATE.format(self.kwargs_to_str(kwargs)) return code class FunctionalMseLossMatcher(BaseMatcher): def generate_code(self, kwargs): if "size_average" in kwargs: size_average = kwargs.pop("size_average") if "True" in size_avera...
激活函数：F.relu(input)：应用 Rectified Linear Unit (ReLU) 激活函数。F.sigmoid(input)：应用 Sigmoid 激活函数。F.tanh(input)：应用 Tanh 激活函数。损失函数：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.c...
PyTorch 的 MSE 损失函数如下。 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...
torch.nn.functional.mse_loss(input, target, size_average=True)torch.nn.functional.margin_ranking_loss(input1, input2, target, margin=0, size_average=True)torch.nn.functional.multilabel_margin_loss(input, target, size_average=True)torch.nn.functional.multilabel_soft_margin_loss(input, target, ...
See MSELoss for details.margin_ranking_losstorch.nn.functional.margin_ranking_loss(input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean') → Tensor [source] See MarginRankingLoss for details.multilabel_margin_losstorch...
compile(optimizer='rmsprop', loss='mse') 1.3 输入数据和训练模型 定义优化目标以后就需要进行数据的输入了,在Keras中是使用fit()方法将数据传递到构建的模型当中的。fit()方法具有较多的参数,其返回值是一个History类对象,这个对象包含两个属性,分别为epoch和history,epoch为训练轮数,history字典类型,包含val_...
nn.MSELoss, nn.CrossEntropyLoss等 (损失函数) 这些类实现了常见的损失函数,如均方误差损失、交叉熵损失等。它们用于计算模型预测与真实值之间的差异。 nn.ReLU, nn.Tanh, nn.Sigmoid等 (激活函数) 这些类实现了常见的激活函数,如ReLU、Tanh和Sigmoid等。你可以将它们作为层的输出或添加到自定义层中。
torch.nn.functional.l1_loss(input, target, size_average=True, reduce=True)→ Tensor详细可见L1Losstorch.nn.functional.mse_loss(input, target, size_average=True, reduce=True)→ Tensor详细可见MSELosstorch.nn.functional.margin_ranking_loss(input, target, size_average=True, reduce=True)→ Tensor...
The L1Loss should create a criterion that measures the MAE error, but in TorchSharp it measures the MSE. I believe that this is incorrect. See the following screenshot comparing TorchSharp and pytorch, which I believe should get identica...