l = loss(output, Y.long()) trainer.zero_grad() l.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1) trainer.step() loss_sum += l.item() * Y.shape[0] print(f"epoch: {epoch}, loss: {loss_sum / len(texts_idx)}") if epoch % 10 == 0: print(predict(prefi...
trainer = torch.optim.SGD(net.parameters(),lr=0.03) # 训练数据 num_epochs = 3 for epoch in range(num_epochs): for x,y in data_iter: l = loss(net(x),y) # net中自带模型参数,不用再传 trainer.zero_grad() l.backward() trainer.step() # step()函数进行一次模型的更新 l=loss(net(...
l = loss(net(X), y) # 计算损失 trainer.zero_grad() l.backward() # 反向传播,求导 trainer.step() # 更新参数 l = loss(net(X), y) # 参数更新后,再次计算损失 print(f'epoch {epoch + 1}, loss {round(float(l.data), 8)}') print(f'模型训练后的w:{net.weight.data}') print(f...
def train_batch(net, X, y, loss, trainer, devices): if isinstance(X, list): # 微调BERT中所需(稍后讨论) X = [(devices[0]) for x in X] else: X = (devices[0]) y = (devices[0]) net.train() trainer.zero_grad() pred = net(X) l = loss(pred, y) l.sum().backward() ...
trainer.zero_grad() l.backward() trainer.step() l = self.loss(self.net(self.features), self.lalels) print(f'epoch:{epoch+1},loss:{l:f}') def print_param(self): print(f'weight:{self.net[0].weight.data}') print(f'bias:{self.net[0].bias.data}') def main(): true_w = ...
optimizer.zero_grad() outputs = student_model(inputs) loss = distillation_loss(inputs, labels) loss.backward() optimizer.step()3.2 Python实现模型压缩实例分析 此处将进一步详细介绍实际项目中如何结合不同压缩技术,针对特定应用场景定制压缩方案,并结合具体代码实例分析压缩前后模型性能的变化,以及如何平衡压缩...
optimizer.zero_grad() # forward + backward + optimize outputs = net(images) loss = criterion(outputs, labels.reshape(-1)) loss.backward() optimizer.step() # print statistics running_loss += loss.item() ifi %100==99:#print every 100 mini-batches ...
optimizer.zero_grad() loss.backward() optimizer.step() if batch % 100 == 0: loss, current = loss.item(), batch * len(X) print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]") 22、Theano Theano 是一个 Python 库,它允许定义、优化和有效地计算涉及多维数组的数学表达式,建在 NumPy...
优化器集成- 支持ZeRO优化器- CPU卸载机制- 通信优化策略 训练器实现 以下是一个完整的分布式训练器实现: classDistributedTrainer: def__init__( self, model: nn.Module, optimizer: Type[torch.optim.Optimizer], world_size: int, gradient_accumulation_steps: int=1 ...
optimizer.zero_grad() loss.backward() optimizer.step() if batch % 100 == 0: loss, current = loss.item(), batch * len(X) print(f"loss:{loss:>7f}[{current:>5d}/{size:>5d}]") 22、 Theano Theano是一个 Python 库,它允许定义、优化和有效地计算涉及多维数组的数学表达式,建在 NumPy 之...