for input, target in dataset: optimizer.zero_grad() output = model(input) loss = loss_fn(output, target) loss.backward() optimizer.step() 1. 2. 3. 4. 5. 6. 构建优化器: 为单个参数进行优化时: 例子: optimizer = optim.SGD(model.parameters(), lr = 0.01, momentum=0.9) 1. 为多个参...
optimizer.step() #更新参数 loss100 += loss.item() #tensor.item()得到的是一个值 #每隔100步先试一下训练效果 if step % 100 == 0: print('[Epoch %d, Batch %5d] loss: %.3f' % (epoch + 1, step + 1, loss100 / 100)) loss100 = 0.0 print("Done Training!") #保存神经网络 torch...
optimizer.step() 对于MNIST,我们执行以下操作 for epoch in epochs: for batch in data: # ---训练步骤开始--- x, y = batch logits = model(x) loss = F.nll_loss(logits, y) # ---训练步骤结束--- optimizer.zero_grad() loss.backward() optimizer.step() 在Lightning中,训练步骤中的所有内容...
可以看到调用了_ optimizer_step,_ optimizer_step经过层层调用,最后会调用torch默认的optimizer.zero_grad,而我们通过 _make_closure得到的closure作为参数传入,具体而言是调用了closure类的call()方法。 defrun(self,optimizer:Optimizer,batch_idx:int,kwargs:OrderedDict)->_OUTPUTS_TYPE:closure=self._make_closure(...
例如,在这里您可以自己进行向后传递梯度 class LitModel(LightningModule): def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None): optimizer.step() optimizer.zero_grad() 对于您可能需要的其他任何内容,我们都有一个广泛的回调系统(https://pytorch-...
自动调用 optimizer.step(), backward, zero_grad() 自动调用 .eval(), enabling/disabling grads 权重加载 保存日志到tensorboard 支持多-GPU、TPU、AMP PL的训练验证测试过程 训练、验证和测试的过程是一样的,就是对三个函数进行重写。 training_step(self, batch, batch_idx) ...
deftraining_step(self, batch, batch_idx, optimizer_idx): ifoptimizer_idx ==0: # do training_step with encoder ifoptimizer_idx ==1: # do training_step with decoder # Truncated back-propagation through time deftraining_step(self, batch, batch_idx, hidd...
detach()} #定义各种metrics def training_step_end(self,outputs): train_acc = self.train_acc(outputs['preds'], outputs['y']).item() self.log("train_acc",train_acc,prog_bar=True) return {"loss":outputs["loss"].mean()} #定义optimizer,以及可选的lr_scheduler def configure_optimizers(...
def training_step(self, batch, batch_nb):x, y = batch loss = F.cross_entropy(self(x), y)tensorboard_logs = {'train_loss': loss} return {'loss': loss, 'log': tensorboard_logs} def configure_optimizers(self):return torch.optim.Adam(self.parameters(), lr=0.02)# 训练!train_loader...
12def training_step(self, batch, batch_idx, optimizer_idx, hiddens): pass 13def training_step_end(self, *args, **kwargs): pass# 接受train_step的返回值 14def training_epoch_end(self, outputs): pass# 接受train_step⼀整个epoch的返回值的列表 15def validation_step(self, batch, batch_idx...