Tensor attribute statistics

 torch.norm Find norm function

import torch
a=torch.tensor([[1,2,3],[-1,1,4]],dtype=torch.float)
print(a.norm(p=1))# Calculate the sum of the absolute values of each element 
print(torch.norm(a,p=1,dim=1))# Specify dimensions , Calculate the sum of the absolute values of each element horizontally 
print(torch.norm(a,p=1,dim=0))# Vertically calculate the sum of the absolute values of each element 

Output

tensor(12.)
tensor([6., 6.])
tensor([2., 3., 7.])

 max Function statistics maximum

a=torch.tensor([[1,2,3],[-1,1,4]],dtype=torch.float)

print(a.max(),a.min(),a.mean())# Return maximum , minimum value , Average 
print(a.argmax())# Returns the index of the maximum value , The default level is 1 dimension 
print(a.argmax(dim=1))# Specify dimensions , Returns the index of the maximum value of each row 
print(a.max(dim=1,keepdim=True))# Specify dimensions , Return the index and maximum value of the maximum value of each row 
# Output results will lose dimensions ,keepdim=True Keep the dimension of the original tensor 
print(a.topk(2,dim=1))# a max Method ,topk Can return n Multiple larger values and their indexes 

Output

tensor(4.) tensor(-1.) tensor(1.6667)
tensor(5)
tensor([2, 2])
torch.return_types.max(
values=tensor([[3.],
        [4.]]),
indices=tensor([[2],
        [2]]))
torch.return_types.topk(
values=tensor([[3., 2.],
        [4., 1.]]),
indices=tensor([[2, 1],
        [2, 1]]))

 a=torch.tensor([[1,2,3],[-1,1,4]],dtype=torch.float)

print(torch.eq(a,a))# Judge whether the two tensors are equal 
print(a>0)# Judge elements and 0 The relationship between 

Output

tensor([[True, True, True],
        [True, True, True]])
tensor([[ True,  True,  True],
        [False,  True,  True]])