print('the accuracy of ', EXPERIMENT_NUM, ' times experiment is :', accs)
时间: 2024-01-18 21:02:16 浏览: 32
这段代码看起来是输出实验的准确率。其中,EXPERIMENT_NUM表示实验的次数,accs是一个包含每次实验的准确率的列表。
可以用以下代码实现:
```
print('the accuracy of', EXPERIMENT_NUM, 'times experiment is:', sum(accs)/len(accs))
```
这会输出实验的平均准确率。
相关问题
if epoch % args.print_epoch == 0: test_stats = evaluate(data_loader_val, model, device) print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%") max_accuracy = max(max_accuracy, test_stats["acc1"]) print(f'Max accuracy: {max_accuracy:.2f}%') train_stats = evaluate(data_loader_train, model, device) print(f"Accuracy of the network on the {len(dataset_train)} train images: {train_stats['acc1']:.1f}%") max_accuracy = max(max_accuracy, train_stats["acc1"]) print(f'train Max accuracy: {max_accuracy:.2f}%')
可以在每次训练过程中加入以下代码来记录并输出模型在训练集上的准确率,并更新最高训练集准确率:
```
if epoch % args.print_epoch == 0:
test_stats = evaluate(data_loader_val, model, device)
print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
max_accuracy = max(max_accuracy, test_stats["acc1"])
print(f'Max accuracy: {max_accuracy:.2f}%')
train_stats = evaluate(data_loader_train, model, device)
print(f"Accuracy of the network on the {len(dataset_train)} train images: {train_stats['acc1']:.1f}%")
max_accuracy = max(max_accuracy, train_stats["acc1"])
print(f'train Max accuracy: {max_accuracy:.2f}%')
```
其中,`train_stats`记录了当前训练过程中模型在训练集上的准确率,每次训练完成后,判断当前的训练集准确率是否超过了最高训练集准确率,如果超过了,则更新最高训练集准确率并输出。
if epoch % args.print_epoch == 0: test_stats = evaluate(data_loader_val, model, device) print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%") max_accuracy = max(max_accuracy, test_stats["acc1"]) print(f'Max accuracy: {max_accuracy:.2f}%')
可以将上述代码与之前提到的打印训练集准确率以及记录最高训练集准确率的代码合并,形成完整的训练代码如下:
```
max_accuracy = 0.0
max_train_accuracy = 0.0
for epoch in range(args.num_epochs):
# Train the model
train(train_loader, model, criterion, optimizer, device)
# Evaluate on the training set
train_stats = evaluate(data_loader_train, model, device)
print(f"Accuracy of the network on the {len(dataset_train)} training images: {train_stats['acc1']:.1f}%")
if train_stats["acc1"] > max_train_accuracy:
max_train_accuracy = train_stats["acc1"]
print(f'Max training accuracy: {max_train_accuracy:.2f}%')
# Evaluate on the validation set
if epoch % args.print_epoch == 0:
test_stats = evaluate(data_loader_val, model, device)
print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
max_accuracy = max(max_accuracy, test_stats["acc1"])
print(f'Max accuracy: {max_accuracy:.2f}%')
```
这样,每次训练过程中都会输出模型在训练集和验证集上的准确率,并记录最高训练集准确率和最高验证集准确率。