for epoch in range(1, num_epochs + 1): train(model, device, train_loader, optimizer, epoch) test_loss, test_acc, pred, target = test(model, device, test_loader) Recall = recall_score(target, pred, average='macro') Precision = precision_score(target, pred, average='macro') F1_score = f1_score(target, pred, average='macro') kappa_score=cohen_kappa_score(target, pred) test_Loss_list.append(test_loss) test_Accuracy_list.append(test_acc) Recall_list.append(Recall) Precision_list.append(Precision) F1_score_list.append(F1_score) kappa_list.append(kappa_score) Loss.append(test_Loss_list) Accuracy.append(test_Accuracy_list) Recall_test.append(Recall_list) Precision_test.append(Precision_list) F1_score_test.append(F1_score_list) kappa_test.append(kappa_list)
时间: 2024-04-11 09:29:20 浏览: 16
这段代码是一个训练模型的循环,它的目的是在每个epoch中执行训练和测试,并记录相关的指标值。在每个epoch中,模型会通过`train`函数进行训练,然后通过`test`函数计算测试集上的损失、准确率、召回率、精确率、F1分数和Kappa分数。这些指标值会被保存到相应的列表中。最后,这些列表会被保存到`Loss`、`Accuracy`、`Recall_test`、`Precision_test`、`F1_score_test`和`kappa_test`中。
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for epoch in range(num_epochs): model.train() train_loss = 0 train_mae = 0 for batch_data, batch_target in train_dataloader: optimizer.zero_grad() output = model(batch
_data) # 前向传播
loss = loss_fn(output, batch_target) # 计算loss
train_loss += loss.item()
mae = mean_absolute_error(output.detach().cpu().numpy(), batch_target.detach().cpu().numpy()) # 计算MAE
train_mae += mae
loss.backward() # 反向传播
optimizer.step() # 更新梯度
train_loss /= len(train_dataloader)
train_mae /= len(train_dataloader)
model.eval() # 切换到评估模式
eval_loss = 0
eval_mae = 0
with torch.no_grad():
for batch_data, batch_target in eval_dataloader:
output = model(batch_data)
loss = loss_fn(output, batch_target)
eval_loss += loss.item()
mae = mean_absolute_error(output.detach().cpu().numpy(), batch_target.detach().cpu().numpy())
eval_mae += mae
eval_loss /= len(eval_dataloader)
eval_mae /= len(eval_dataloader)
print(f"Epoch {epoch+1}/{num_epochs}, Train Loss: {train_loss:.4f}, Train MAE: {train_mae:.4f}, Eval Loss: {eval_loss:.4f}, Eval MAE: {eval_mae:.4f}")
if eval_loss < best_eval_loss:
best_eval_loss = eval_loss
torch.save(model.state_dict(), save_path)
print("Save model successfully!")
for epoch in range(resume_epoch, num_epochs)
在这个代码片段中,for循环用于遍历从resume_epoch到num_epochs的每一个epoch。在每个epoch中,通过调用train_dataloader来获取训练数据的每个batch,并使用训练步骤(training_step)来计算出一个out。然后通过backward函数计算梯度并应用梯度更新(optimizer.step()),同时清除梯度(optimizer.zero_grad())。此外,还会根据需要在val/test步骤上自动记录(metrics)。最后,在每个epoch中计算epoch级别的操作(epoch_metric)。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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