emb_user.shape[0], emb_anime.shape[0]) predicted = create_sparse_matrix(predict(df, emb_user, emb_anime), emb_user.shape[0], emb_anime.shape[0], 'prediction') return np.sum((Y-predicted).power(2))/df.shape[0]预测
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...
🐛 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...
print("Matrix的形状:", matrix.shape # 输出torch.Size([2,3]) 2x3的矩阵 1. 2. 3. 4. 5. 6. 7. 1.4高维张量: import torch tensor_3d = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) # 创建一个2x2x2的3维张量 ...
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)returnout ...
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}") ...
# Batch matrix multiplication: (b*m*n) * (b*n*p) -> (b*m*p)result = torch.bmm(tensor1, tensor2) # Element-wise multiplication.result = tensor1 * tensor2 计算两组数据之间的两两欧式距离 利用broadcast机制 dist = torch.sqrt(torch.sum((X1[:,None,:] ...
tensor([[1., 2., 3.], [4., 5., 6.]]) #指定device my_tensor=torch.tensor([[1,2,3],[4,5,6]],dtype=torch.float32,device='cuda') print(my_tensor) tensor([[1., 2., 3.], [4., 5., 6.]], device='cuda:0') ...
(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 ...