inputs, labels = data outputs = net(inputs) loss = loss_functoin(outputs, labels) _, predicted = torch.max(outputs.data, 1) total_val += labels.size(0) correct_val += (predicted == labels).squeeze().sum().numpy() loss_val += loss.item() valid_curve.append(loss_val) print(...
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-9b/blob/main/modeling_chatglm.py的ChatGLMForConditionalGeneration类的forward函数),这里来看...
Interpreted as binary (sigmoid) output with outputs of size [B, H, W]. labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1) classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average. per_image: compute the loss per imag...
In deep learning, a loss function, also known as a cost or objective function, is a crucial component that quantifies the dissimilarity between the predicted outputs generated by a neural network and the actual target values in a given dataset. The primary purpose of a loss function is to ...
# 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) ...
losses = [Loss(output, label)foroutput, labelinzip(outputs, labels)] test_loss.update(0, losses) metric.update(labels, outputs) metric_top5.update(labels, outputs) _, test_top1_acc = metric.get() _, test_top5_acc = metric_top5.get() ...
loss_fn = torch.nn.CrossEntropyLoss(reduction=reduction) loss_fn = loss_fn.cuda(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()) ...
PyTorch进行梯度的计算,只能对标量进行梯度计算,若直接使用 y.backward() 会报错:grad can be implicitly created only for scalar outputs。这一问题的解决方法就是先使用.sum()再反向传播。例如是一个标量,是能够进行梯度计算的,而例如这是二维的,pytorch并...
for epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): # 前向传播 outputs = model(images) # 计算损失 loss = loss_fn(outputs, labels) # 记录损失 lr.record(loss.item()) 可视化Loss曲线: lr.plot() 需要注意的是,上述代码中的model和loss_fn需要根据具体任务...
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_...