torch.zero_like用法 返回和输入矩阵形状相同的全0矩阵。相当于torch.zeros(x.size())
nn.init.constant_(m.bias,0)forminself.modules():ifisinstance(m, BasicBlock): m.bn2.weight = nn.Parameter(torch.zeros_like(m.bn2.weight))ifisinstance(m, Bottleneck): m.bn3.weight = nn.Parameter(torch.zeros_like(m.bn3.weight))ifisinstance(m, nn.Linear): m.weight.data.normal_(0,...
这通常比torch.zeros更快，但你需要随后调用zero_()来将它置零。这样做可能在某些情况下更快，因为它...
torch.ones_like()与torch.ones()功能:创建全1张量,用法与zero相同。 torch.full() 和 torch.full_like() torch.full(size, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) torch.full_like(input, fill_value, dtype=None, layout=None, device=None, req...
torch.nonzero(x) torch.addcmul() torch.addcmul(input, tensor1, tensor2, *, value=1, out=None) 用tensor2对tensor1逐元素相乘,并对结果乘以标量值value然后加到tensor 0.4117*1.0660*0.1+1.8626 = 1.9065 torch.kthvalue() 取input指定维度上第k个最小值 torch.kthvalue(input, k, dim=None, out=No...
x.grad.zero_() y = x * x y.sum().backward() # 等价于y.backward(torch.ones(len(x))) # 也可以y.backward(torch.ones_like(x)) x.grad # 分离计算 # 将计算移动到记录的计算图之外 # 在这里,我们可以分离y来返回一个新变量u,该变量与y具有相同的值,但丢弃计算图中如何计算y的任何信息。换...
import torch import triton import triton.language as tl @triton.autotune(configs=[ triton.Config(kwargs={'BLOCK_SIZE': 16}, num_warps=8, num_stages=4), triton.Config(kwargs={'BLOCK_SIZE': 32}, num_warps=8, num_stages=4), ], key=['n_elements'], reset_to_zero=["output_ptr"]...
# 需要导入模块: import torch [as 别名]# 或者: from torch importeye[as 别名]deftest_weighted_midpoint_weighted_zero_sum(_k, lincomb):manifold = stereographic.Stereographic(_k, learnable=True) a = manifold.expmap0(torch.eye(3,10)).detach().requires_grad_(True) ...
git地址: 一:介绍torch 1.常见的机器学习框架 2.能带来什么 GPU加速 自动求导 importtorchfromtorchimportautograd x= torch.tensor(1.) a= torch.tensor(1., requires_grad=True) b= torch.tensor(2., requires_grad=True) c= torch.tensor(3., requires_grad=True) ...
zero_grad() scheduler_dict[p].step() # Register the hook onto every parameter for p in model.parameters(): if p.requires_grad: p.register_post_accumulate_grad_hook(optimizer_hook) layer_wise_flag = True else: raise ValueError(f"Optimizer {args.optimizer} not supported") if...