inputs, labels = data outputs = net(inputs) # backward optimizer.zero_grad() loss = loss_functoin(outputs, labels) loss.backward() # update weights optimizer.step() # 统计分类情况 _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels)....
先通过nn.CrossEntropyLoss构建损失函数赋给loss_function,紧接着在训练过程中通过 loss = loss_functoin(outputs, labels) #②处 1. 进行计算其损失函数,输入神经网络模型的输出outputs的值和标签进行loss。 在①②处设置断点,step into①处时,进入loss.py,调用class CrossEntropyLoss类,继承_WeightedLoss类,也就...
loss_fn = nn.MSELoss() # 设置模型为评估模式 model.eval() # 禁止梯度计算 with torch.no_grad(): # 计算验证集损失 val_loss = 0.0 for inputs, labels in val_data: outputs = model(inputs) val_loss += loss_fn(outputs, labels) val_loss /= len(val_data) ``` 在上述代码中,我们首先...
模型执行:inputs 包含输入数据,如 input_ids 和labels,然后调用模型的 forward 方法生成输出。 outputs 的结构: logits:模型的预测结果张量。 loss:根据 inputs 中的labels 自动计算的损失值。 past_key_values:用于缓存过去的状态(例如在语言模型中)。 比如GLM4V模型就会调用https://huggingface.co/THUDM/glm-4v...
def loss_fn(outputs, targets): return nn.BCEWithLogitsLoss()(outputs, targets.view(-1, 1)) Example #25Source File: networks.py From EvolutionaryGAN-pytorch with MIT License 5 votes def __init__(self, loss_mode, which_net, which_D, target_real_label=1.0, target_fake_label=0.0): ...
loss = loss_fn(outputs_torch, targets_torch) loss = loss.detach().cpu().numpy() print(i, outputs.sum(), targets.sum(), outputs.mean(), targets.mean(), loss.sum(), loss.mean()) Output: After running the above code, we get the following output in which we can see that the cro...
logpt = -self.ce_fn(preds, labels) pt = torch.exp(logpt) loss = -((1 - pt) ** self.gamma) * self.alpha * logpt return loss OHEM 在线困难样本挖掘,即根据loss的大小,选择有较大loss的像素反向传播,较小loss的像素梯度为0。 def focal_loss(self, output, target, alpha, gamma, OHEM_...
# 需要导入模块: from apex import amp [as 别名]# 或者: from apex.amp importscale_loss[as 别名]def_forward(self, args, inputs, labels, masker, model, backprop=True):outputs = model(inputs, masked_lm_labels=labels)ifargs.mlmelsemodel(inputs, labels=labels) ...
LetI={1,...,n}andD:={(xxi,yi),xxi∈Rp,yi∈{−1,1},i∈I}be a finite dataset ofnobservations withpfeatures and binary labels. We introduce the notation, largely based on Bertsimas and Dunn (2017), D’Onofrio et al. (2024), used for the formulation of the Loss-Optimal CTs lear...
In PyTorch, inputs, outputs, and parameters of the model are encoded using tensors, which means we must convert our Numpy arrays to tensors. That’s the first thing we do in the code below, and then we build theneural networkand print its dimensions. ...