acc = binary_acc(predictions, batch.label) epoch_loss += loss * len(batch.label) epoch_acc += acc * len(batch.label) total_len += len(batch.label) return epoch_loss / total_len, epoch_acc / total_len 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17...
(3)计算准确率时候如下: defbinary_acc(self, preds, y): preds=torch.round(torch.sigmoid(preds)) correct=torch.eq(preds, y).float() acc= correct.sum() /len(correct)returnacc (4)预测时候 preds = torch.round(torch.sigmoid(preds))
import torch.optim as optim#定义优化器和损失optimizer = optim.Adam(model.parameters())criterion = nn.BCELoss()#定义度量def binary_accuracy(preds, y): #四舍五入到最接近的整数 rounded_preds = torch.round(preds) correct = (rounded_preds == y).float() acc = correct.sum() /...
(3)计算准确率时候如下: def binary_acc(self, preds, y): preds = torch.round(torch.sigmoid(preds)) correct = torch.eq(preds, y).float() acc = correct.sum() / len(correct) return acc 1. 2. 3. 4. (4)预测时候 preds = torch.round(torch.sigmoid(preds)) 1....
()) acc = binary_acc(logits, b_labels) # (predict, label) avg_acc.append(acc) optimizer.zero_grad() loss.backward() clip_grad_norm_(model.parameters(), 1.0) # 大于1的梯度将其设为1.0, 以防梯度爆炸 optimizer.step() # 更新模型参数 scheduler.step() # 更新learning rate avg_acc = ...
acc = utils.binary_acc(torch.max(pred, dim=1)[1], y_batch) avg_acc.append(acc) # 记录该批次正确率 # 使用损失函数计算损失值,预测值要放在前 loss = loss_function(pred, y_batch) # 清楚之前的梯度值 opt.zero_grad() # 反向传播 ...
其官网对文件格式的详细内容进行了解释，本质上就是一个JSON文件加上若干binary形式的buffer。对于tensor...
Pytorch-使用Bert预训练模型微调中文文本分类 Pytorch-使⽤Bert预训练模型微调中⽂⽂本分类 渣渣本跑不动，以下代码运⾏在Google Colab上。neg.txt和pos.txt各5000条酒店评论，每条评论⼀⾏。安装transformers库 !pip install transformers 导包，设定超参数 1import numpy as np 2import random 3import ...
保证Dropout在评估模型下with torch.no_grad(): # 禁止梯度计算for batch in iterator:predictions = model(batch.text).squeeze(1) # 计算结果loss = criterion(predictions, batch.label) # 计算损失acc = binary_accuracy(predictions, batch.label) # 计算精确率epoch_loss += loss.item()epoch_acc += ...
torch.autograd.backward ( tensors, grad_tensors=None,retain_graph=None,create_graph=False) 功能:自动求取梯度 tensors : 用于求导的张量,如 loss retain_graph : 保存计算图 create_graph:创建导数计算图,用于高阶求导 grad_tensors :多梯度权重(用于设置权重) ...