y = t.ones(1, requires_grad=True) y.requires_grad #True x = x.detach() #分离之后 x.requires_grad #False y = x+y #tensor([2.]) y.requires_grad #我还是True y.retain_grad() #y不是叶子张量,要加上这一行 z = t.pow(y, 2) z.backward() #反向传播 y.grad #tensor([4.]) ...
torch.tensor(data,dtype=None,device=None,requires_grad=False,pin_memory=False) →Tensor torch.zeros(*size,out=None,dtype=None,layout=torch.strided,device=None,requires_grad=False) →Tensor torch.ones(*size,out=None,dtype=None,layout=torch.strided,device=None,requires_grad=False) →Tensor...
这次版本的主要更新一些性能的优化，包括权衡内存计算，提供 Windows 支持，24个基础分布，变量及数据类型...
clone()函数返回一个和源张量同shape、dtype和device的张量，与源张量不共享数据内存，但提供梯度的回溯...
torch.checkpointRuntimeError: element 0 of tensors does not require grad and does not have a grad_fn 高进 1 人赞同了该文章 (5) outputs += (router_logits, ) return outputs (6) return outputs, routers (1) outputs, router_logits = block( hidden_states, alibi, causal_mask, layer_past,...
我们用requires_grad=True创建两个张量a和b。 这向autograd发出信号,应跟踪对它们的所有操作。 import torch a = torch.tensor([2., 3.], requires_grad=True) b = torch.tensor([6., 4.], requires_grad=True) print(f"a:{a}") print(f"b:{b}") # 我们从a和b创建另一个张量Q Q = 3*a*...
3. 使用requires_grad=False # 1. 整个网络requires_grad=False net = model().cuda() for p in net.parameters(): p.requires_grad = False optimizer = torch.optim.Adam(net.parameters(), lr=0.01) 结果:RuntimeError: element 0 oftensorsdoes not require grad and does not have a grad_fn。这...
requires_grad memo[id(self)] = new_tensor return new_tensor def __reduce_ex__(self, proto): relevant_args = (self,) from torch.overrides import has_torch_function, handle_torch_function if type(self) is not Tensor and has_torch_function(relevant_args): return handle_torch_function(...
🐛 Bug To Reproduce Steps to reproduce the behavior: Create tensor Create new_full with requires_grad=True New full tensor should require gradient as per documentation tensor = torch.ones((2,)) new_tensor = tensor.new_full((3, 4), 3.14159...
label=torch.tensor(data=index%1000,dtype=torch.int64) returnrand_image,label train_set=FakeDataset() batch_size=128 num_workers=12 train_loader=DataLoader( dataset=train_set, batch_size=batch_size, num_workers=num_workers, pin_memory=True ...