def train(model, tokenizer, data_loader, optimizer, criterion, device, max_grad_norm=1.0, DEBUGGING_IS_ON=False):model.train()total_loss = 0for batch in data_loader:optimizer.zero_grad() input_data = batch['input_ids'].clone().to(de...
「torch.zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False):这个是创建与 input 同形状的全 0 张量」 代码语言:javascript 代码运行次数:0 运行 AI代码解释 t=torch.zeros_like(out_t)# 这里的input要是个张量print(t)tensor([[0,0,0],[0,0,0],[0,0,0]]) 除了全 ...
gen_opt.zero_grad() fake_noise_2 = get_noise(cur_batch_size, z_dim, device=device) fake_2 = gen(fake_noise_2) disc_fake_pred = disc(fake_2) gen_loss = criterion(disc_fake_pred, torch.ones_like(disc_fake_pred)) gen_loss.backward() ...
self.normalize = normalize_to_neg_one_to_one if auto_normalize else identityself.unnormalize = unnormalize_to_zero_to_one if auto_normalize else identity @torch.inference_mode()def p_sample(self, x: torch.Tensor, timestamp: int) -> torch.Tensor:b...
optimizer.zero_grad() output = model(data) train_loss = criterion(output, target) train_loss.backward()# 以上均和单机版代码相同。接下来遍历本机模型的parameters,并获取grad梯度,发送到master node,并从master node获取平均值后更新gradforpinmodel.parameters():# 将grad值发送到master nodedist.gather(p...
optimizer.zero_grad()input_data=batch['input_ids'].clone().to(device)attention_mask=batch['attention_mask'].clone().to(device)target=input_data[:, 1:]input_data=input_data[:, :-1] # Pad all the sequencesinthe batch:input_data=pad_sequences_3d(input_data,pad_value=tokenizer.pad_tok...
5、torch.is_nonzero() torch.is_nonzero(input) 如果输入张量不为0,则返回true,否则为False,注意这里必须是单张量,否则会抛出异常 input:待判断张量 示例: >>>torch.is_nonzero(torch.tensor([0]))False>>>torch.is_nonzero(torch.tensor([0.0]))False>>>torch.is_nonzero(torch.tensor([1]))True>...
def train(model, iterator, optimizer, criterion): #初始化 epoch_loss = 0 epoch_acc = 0 #设置为训练模式 model.train() for batch in iterator: #在每一个batch后设置0梯度 optimizer.zero_grad() text, text_lengths = batch.text #转换成一维张量 predictions = m...
索引和数据筛选 torch.nonzero(input, ***, out=None, as_tuple=False) → LongTensor or tuple of LongTensors 返回非零元素的索引位置 索引和数据筛选 torch.narrow(input, dim, start, length) → Tensor 在dim 维度上,从给定的start位置,选取 start + length 长度的元素 切片 torch.chunk(input, chunk...
data.zero_() self.layer.weight.data += 1 def forward(self, x): return self.layer(x) net = Net() BS = 6 input = torch.zeros(BS, 1).to(torch.float32) + 1 output = net(input) target = torch.zeros_like(input) loss = (0.5 * (output - target) ** 2).sum() loss.backward(...