矩阵乘法的成本很高,我假设特征分解和对角线化将加快整个过程。但是令我惊讶的是,这种据说改进的方法花费了更多时间。我在这里错了吗? import timeit mysetup = ''' import numpy as np from numpy import linalg as LA from numpy.linalg import matrix_power EXP = 5 # no. of time linear transformation i...
torch.matmul(tensor1, tensor2, out=None) → Tensor torch.matrix_power(input, n) → Tensor torch.matrix_rank(input, tol=None, bool symmetric=False) → Tensor torch.mm(mat1, mat2, out=None) → Tensor torch.mv(mat, vec, out=None) → Tensor torch.orgqr(a, tau) → Tensor torch.pin...
对特征进行预处理: k=2norm_x=torch.mm(torch.matrix_power(s,k),feature) 最后,搭建模型: classSGC(nn.Module):def__init__(self,in_feats,out_feats):super(SGC,self).__init__()self.softmax=nn.Softmax(dim=1)self.w=nn.Linear(in_feats,out_feats)defforward(self,x):out=self.w(x)retur...
#matrix_exp=torch.rand((5,5)) matrix_exp=torch.tensor([[1,2],[3,4]],dtype=torch.float) print(matrix_exp.matrix_power(3)) #上面等价于矩阵做3次自身乘法 torch.mm(matrix_exp,matrix_exp).mm(matrix_exp) tensor([[ 37., 54.], [ 81., 118.]]) tensor([[ 37., 54.], [ 81.,...
'matrix_power', 'max', 'mean', 'median', 'min', 'mm', 'mode', 'mul', 'mul_', 'multinomial', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'narrow', 'narrow_copy', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'nelement', 'new', 'new_empty', 'new_full', 'new_ones...
🐛 Describe the bug A runtime error occurs when using the torch._C._linalg.linalg_matrix_power function withtorch.compile mode. The function works as expected outside of torch.compile, but raises an exception when compiled with specific s...
(2, 2) # random matrix m2 = torch.tensor([[3., 0.], [0., 3.]]) # three times identity matrix print('\nVectors & Matrices:') print(torch.cross(v2, v1)) # negative of z unit vector (v1 x v2 == -v2 x v1) print(m1) m3 = torch.matmul(m1, m2) print(m3) # 3 ...
main 4496Branches1192Tags Code Folders and files Name Last commit message Last commit date Latest commit GeorgeWigley and pytorchmergebot Update CPU tolerance for f16 triplet margin loss (#147742) Mar 7, 2025 b85ae06·Mar 7, 2025 History ...
layer = nn.Linear(3, 4) X = torch.rand_like(layer.weight) print(f"Initialization matrix:\n{X}") parametrize.register_parametrization(layer, "weight", PruningParametrization(layer.weight)) layer.weight = X print(f"\nInitialized weight:\n{layer.weight}") ...
# Matrix multiplcation: (m*n) * (n*p) * -> (m*p).result = torch.mm(tensor1, tensor2) # Batch matrix multiplication: (b*m*n) * (b*n*p) -> (b*m*p)result = torch.bmm(tensor1, tensor2) # Element-wise multiplication.result = tensor1 * tensor2 ...