「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 ...
6.2.3 torch.nonzero:返回非零元素的索引位置 6.2.5 条件选择:torch.where 6.2.6 index_select vs gather 6.2.6.1 index_select 6.2.6.2 gather 6.2.6.2.1 gather 的用途 6.2.8 torch.take:根据指定的索引选取数值 6.3 切片(重要) 6.3.1 torch.chunk:拆分成指定的个数 6.3.2 torch.split:按照指定的元素...
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...
for batch_idx, (data, target) in enumerate(train_loader):# 获取数据data, target =data.to(device), target.to(device)optimizer.zero_grad() output = model(data)# 计算loss train_loss = criterion(output, target) # 更新权值 train_loss.backward() optimizer.step() 多节点训练VGG11模型:总述 接...
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() ...
(1,1,bias=False)self.layer.weight.data.zero_()self.layer.weight.data+=1defforward(self,x):returnself.layer(x)net=Net()BS=6input=torch.zeros(BS,1).to(torch.float32)+1output=net(input)target=torch.zeros_like(input)loss=(0.5*(output-target)**2).sum()loss.backward()print(f'grad ...
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...
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...
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>...
for data in dataloader:optimizer.zero_grad()output = ddp_model(data)loss = nn.MSELoss()(output, torch.zeros_like(output))loss.backward()optimizer.step()dist.destroy_process_group()if __name == ‘__main‘:world_size = 2mp.spawn(...