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
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...
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...
使用PyTorch计算梯度数值 PyTorch的Autograd模块实现了深度学习的算法中的向传播求导数,在张量(Tensor类)上的所有操作,Autograd都能为他们自动提供微分,简化了手动计算导数的复杂过程。 在0.4以前的版本中,Pytorch使用Variable类来自动计算所有的梯度Variable类主要包含三个属性: data:保存Variable所包含的Tensor;grad:保存dat...
🐛 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...
tensor初始化 #定义一个tensor my_tensor=torch.tensor([[1,2,3],[4,5,6]]) print(my_tensor) tensor([[1, 2, 3], [4, 5, 6]]) #指定tensor的数据类型 my_tensor=torch.tensor([[1,2,3],[4,5,6]],dtype=torch.float32) print(my_tensor) ...
mm(torch.matrix_power(s, k), feature) 最后,搭建模型: class SGC(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) def forward(self, x): out = self.w(x) return ...
A replacement for NumPy to use the power of GPUs. A deep learning research platform that provides maximum flexibility and speed. Elaborating Further: A GPU-Ready Tensor Library If you use NumPy, then you have used Tensors (a.k.a. ndarray). ...
# 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,:] ...
(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 ...