torch.topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor)

import torch

pred = torch.randn((4, 5))
print(pred)
values, indices = pred.topk(1, dim=1, largest=True, sorted=True)
print(indices)
# 用max得到的结果，设置keepdim为True，避免降维。因为topk函数返回的index不降维，shape和输入一致。
_, indices_max = pred.max(dim=1, keepdim=True)

print(indices_max == indices)
# pred
tensor([[-0.1480, -0.9819, -0.3364, 0.7912, -0.3263],
    [-0.8013, -0.9083, 0.7973, 0.1458, -0.9156],
    [-0.2334, -0.0142, -0.5493, 0.0673, 0.8185],
    [-0.4075, -0.1097, 0.8193, -0.2352, -0.9273]])
# indices, shape为 【4,1】,
tensor([[3],  #【0,0】代表 第一个样本最可能属于第一类别
    [2],  # 【1, 0】代表第二个样本最可能属于第二类别
    [4],
    [2]])
# indices_max等于indices
tensor([[True],
    [True],
    [True],
    [True]])

import torch

pred = torch.randn((4, 5))
print(pred)
values, indices = pred.topk(2, dim=1, largest=True, sorted=True) # k=2
print(indices)
# pred
tensor([[-0.2203, -0.7538, 1.8789, 0.4451, -0.2526],
    [-0.0413, 0.6366, 1.1155, 0.3484, 0.0395],
    [ 0.0365, 0.5158, 1.1067, -0.9276, -0.2124],
    [ 0.6232, 0.9912, -0.8562, 0.0148, 1.6413]])
# indices
tensor([[2, 3],
    [2, 1],
    [2, 1],
    [4, 1]])