scheduler.step(crossval_batch_loss)

scheduler.step(crossval_batch_loss) 是一个用于调整习率的函数调用。它将传入的参数 `crossval_batch_loss` 用于更新优化器的学习率，并根据预设的学习率调度策略进行相应的调整。具体的学习率调度策略可以是固定学习率、学习率衰减、余弦退火等等，具体取决于你在创建 scheduler 对象时使用的调度器类型。通过调用 `scheduler.step()`，你可以在训练过程中动态地更新和调整学习率，以优化模型的训练效果。

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def train(self): self.loss.step() epoch = self.scheduler.last_epoch + 1 learn_rate = self.scheduler.get_last_lr()[0] self.ckp.write_log( '[Epoch {}]\tLearning rate: {:.2e}'.format(epoch, Decimal(learn_rate)) ) self.loss.start_log() self.model.train() timer_data, timer_model = utils.timer(), utils.timer() # timer_model.tic() for batch, (lr, hr, file_names) in enumerate(self.loader_train): lr, hr = self.prepare([lr, hr]) timer_data.hold() timer_model.tic() self.optimizer.zero_grad() sr = self.model(lr) loss = self.loss(sr, hr) if loss.item() < self.args.skip_threshold * self.error_last: loss.backward() self.optimizer.step() else: print('Skip this batch {}! (Loss: {})'.format( batch + 1, loss.item() )) timer_model.hold() if (batch + 1) % self.args.print_every == 0: self.ckp.write_log('[{}/{}]\t{}\t{:.1f}+{:.1f}s'.format( (batch + 1) * self.args.batch_size, len(self.loader_train.dataset), self.loss.display_loss(batch), timer_model.release(), timer_data.release())) timer_data.tic() self.scheduler.step() self.loss.end_log(len(self.loader_train)) self.error_last = self.loss.log[-1, -1]

这段代码是在训练模型，其中self.loss.step()是更新损失函数，self.scheduler.last_epoch是获取当前epoch数，self.scheduler.get_last_lr()[0]是获取当前学习率。self.ckp.write_log()是将当前epoch数和学习率写入日志文件。self.loss.start_log()是开始记录训练日志。self.model.train()是将模型设置为训练模式。timer_data和timer_model是记录数据加载和模型计算时间的计时器。

详细分析一下python代码:import torch.optim as optim criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=0.01, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10, verbose=True, min_lr=0) loss_hist, acc_hist = [], [] loss_hist_val, acc_hist_val = [], [] for epoch in range(140): running_loss = 0.0 correct = 0 for data in train_loader: batch, labels = data batch, labels = batch.to(device), labels.to(device) optimizer.zero_grad() outputs = net(batch) loss = criterion(outputs, labels) loss.backward() optimizer.step() # compute training statistics _, predicted = torch.max(outputs, 1) correct += (predicted == labels).sum().item() running_loss += loss.item() avg_loss = running_loss / len(train_set) avg_acc = correct / len(train_set) loss_hist.append(avg_loss) acc_hist.append(avg_acc) # validation statistics net.eval() with torch.no_grad(): loss_val = 0.0 correct_val = 0 for data in val_loader: batch, labels = data batch, labels = batch.to(device), labels.to(device) outputs = net(batch) loss = criterion(outputs, labels) _, predicted = torch.max(outputs, 1) correct_val += (predicted == labels).sum().item() loss_val += loss.item() avg_loss_val = loss_val / len(val_set) avg_acc_val = correct_val / len(val_set) loss_hist_val.append(avg_loss_val) acc_hist_val.append(avg_acc_val) net.train() scheduler.step(avg_loss_val) print('[epoch %d] loss: %.5f accuracy: %.4f val loss: %.5f val accuracy: %.4f' % (epoch + 1, avg_loss, avg_acc, avg_loss_val, avg_acc_val))

这段代码是一个基于PyTorch的神经网络训练过程。代码中使用了torch.optim模块中Adam优化器和ReduceLROnPlateau学习率调度器。其中，Adam优化器用于优化网络的参数，而ReduceLROnPlateau调度器用于自动调整学习率以提高训练效果。代码中使用nn.CrossEntropyLoss()作为损失函数，用于计算输出结果与标签之间的差距。

接下来的代码中使用了两个循环，一个是对训练数据集的循环，另一个是对验证数据集的循环。在训练数据集循环中，首先将数据集分成一个个batch，然后将batch和对应的标签传入网络进行前向传播，计算损失值，然后进行反向传播和参数更新。在这个过程中，记录了每个batch的正确预测个数和损失值，最后计算平均损失和准确率，并将其保存在loss_hist和acc_hist列表中。

在验证数据集循环中，同样将数据集分成一个个batch，然后将batch和对应的标签传入网络进行前向传播，计算损失值，并计算正确预测个数。最后将每个batch的平均损失和准确率记录在loss_hist_val和acc_hist_val列表中。

在每个epoch结束后，调用scheduler.step(avg_loss_val)方法来更新学习率，并打印出当前epoch的训练和验证结果。其中，avg_loss和avg_acc记录了该epoch的训练结果，avg_loss_val和avg_acc_val记录了该epoch的验证结果。