基于python及pytorch中乘法的使用详解

A = np.array([[1, 2, 3], [2, 3, 4]])
B = np.array([[1, 0, 1], [2, 1, -1]])
C = np.array([[1, 0], [0, 1], [-1, 0]])
 
A * B : # 对应位置相乘
np.array([[ 1, 0, 3], [ 4, 3, -4]]) 
 
A.dot(B) :  # 矩阵乘法 
ValueError: shapes (2,3) and (2,3) not aligned: 3 (dim 1) != 2 (dim 0)
 
A.dot(C) : # 矩阵乘法  | < -- > np.dot(A, C)
np.array([[-2, 2],[-2, 3]])

A = torch.tensor([[1, 2, 3], [2, 3, 4]])
B = torch.tensor([[1, 0, 1], [2, 1, -1]])
C = torch.tensor([[1, 0], [0, 1], [-1, 0]])
 
# 矩阵乘法
torch.mm(mat1, mat2, out=None) <--> torch.matmul(mat1, mat2, out=None)
eg : 
  torch.mm(A, B)   : RuntimeError: size mismatch, m1: [2 x 3], m2: [2 x 3]
  torch.mm(A, C)   : tensor([[-2, 2], [-2, 3]])
  torch.matmul(A, C) : tensor([[-2, 2], [-2, 3]])
 
# 点乘
torch.mul(mat1, mat2, out=None)
 
eg :
  torch.mul(A, B) : tensor([[ 1, 0, 3], [ 4, 3, -4]])
  torch.mul(A, C) : RuntimeError: The size of tensor a (3) must match the size of tensor b (2) at non-singleton dimension 1