print(f"Epoch {epoch + 1}, Learning rate: {optimizer.param_groups[0]['lr']}")

这是一段使用Python中的f-string格式化字符串的代码，它的作用是输出当前的训练epoch以及当前使用的学习率。其中，{epoch + 1}表示当前的epoch加1，{optimizer.param_groups[0]['lr']}表示当前优化器的学习率。这段代码可以在训练神经网络模型时用于监测学习率的变化情况。

相关问题

def adjust_learning_rate(optimizer, epoch): lr = 0.001 print(lr) for param_group in optimizer.param_groups: param_group['lr'] = lr什么意思

这段代码是一个学习率调整函数，用于在训练过程中动态地调整优化器的学习率。

在深度学习中，学习率是一个非常重要的超参数，它决定了模型在每一次参数更新时的步长大小。如果学习率设置过大，会导致模型无法收敛；如果学习率设置过小，会导致模型收敛缓慢。

在这个函数中，将学习率初始化为 0.001，然后将优化器中所有参数组的学习率调整为这个值。在实际训练中，可以根据训练集的大小、模型的复杂度、训练的速度等因素来动态地调整学习率，以便更好地训练模型。通常情况下，学习率会随着训练的进行而逐渐减小，以便模型更好地收敛。

详细解释每一句代码if i % 20 == 0 or i == total_step: print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], ' '[lateral-2: {:.4f}, lateral-3: {:0.4f}, lateral-4: {:0.4f}]'. format(datetime.now(), epoch, opt.epoch, i, total_step, loss_record2.show(), loss_record3.show(), loss_record4.show())) print('lr: ', optimizer.param_groups[0]['lr']) save_path = 'snapshots/{}/'.format(opt.train_save) os.makedirs(save_path, exist_ok=True) if (epoch+1) % 1 == 0: meanloss = test(model, opt.test_path) if meanloss < best_loss: print('new best loss: ', meanloss) best_loss = meanloss torch.save(model.state_dict(), save_path + 'TransFuse-%d.pth' % epoch) print('[Saving Snapshot:]', save_path + 'TransFuse-%d.pth'% epoch) return best_loss

Sure, here's a detailed explanation of each line of code:

if i % 20 == 0 or i == total_step:

This line checks if the current step `i` is a multiple of 20 or if it is the last step `total_step`. If either condition is true, the following code block will be executed.

print('{} Epoch [{:03d}/{:03d}], Step [{:04d}/{:04d}], '
      '[lateral-2: {:.4f}, lateral-3: {:0.4f}, lateral-4: {:0.4f}]'.
      format(datetime.now(), epoch, opt.epoch, i, total_step, 
             loss_record2.show(), loss_record3.show(), loss_record4.show()))

This line prints the current date and time, epoch number, step number, and loss values for three different lateral connections (lateral-2, lateral-3, lateral-4) in a specific format. The `datetime.now()` function gets the current date and time, while the other variables such as `epoch`, `opt.epoch`, `i`, `total_step`, `loss_record2`, `loss_record3`, and `loss_record4` are defined elsewhere in the code.

print('lr: ', optimizer.param_groups[0]['lr'])

This line prints the current learning rate of the optimizer, which is stored in the optimizer's `param_groups` attribute.

save_path = 'snapshots/{}/'.format(opt.train_save)
os.makedirs(save_path, exist_ok=True)

These lines create a directory to save the model snapshots. The `opt.train_save` variable specifies the name of the directory, and the `os.makedirs()` function creates the directory if it doesn't already exist.

if (epoch+1) % 1 == 0:

This line checks if the current epoch plus one is a multiple of one (which it always will be), and if so, executes the following code block. This code block is executed every epoch.

meanloss = test(model, opt.test_path)

This line calls the `test()` function with the trained model and the specified test dataset path `opt.test_path`, and calculates the mean loss value over the test dataset.

if meanloss < best_loss:
    print('new best loss: ', meanloss)
    best_loss = meanloss
    torch.save(model.state_dict(), save_path + 'TransFuse-%d.pth' % epoch)
    print('[Saving Snapshot:]', save_path + 'TransFuse-%d.pth'% epoch)

This code block checks if the mean loss value is lower than the previous best loss value. If so, it updates the best loss value, saves the current model state dictionary to a file in the specified directory, and prints a message indicating that a new snapshot has been saved.

return best_loss

This line returns the best loss value after the training loop is complete.