import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.nn import CrossEntropyLoss
import math

shift_logits = torch.zeros((16, 4096))
shift_logits[:, 2] = 10.0
shift_labels = (torch.ones(16) * 2).long()
loss = CrossEntropyLoss()(shift_logits, shift_labels)
print(loss)


loss = nn.CrossEntropyLoss()
input = torch.tensor([[1.0, 2.0, 3.0]])
target = torch.tensor([0]).long()
output = loss(input, target)
print(output)


# torch.manual_seed(32)
# criterion = nn.CrossEntropyLoss()
# output = torch.randn(1, 5)
# label = torch.ones(1, dtype=torch.long)*3
# loss = criterion(output, label)

# print("网络输出为5类:")
# print(output)
# print("要计算label的类别:")
# print(label)
# print("计算loss的结果:")
# print(loss)

# first = 0
# for i in range(1):
#     first = -output[i][label[i]]
# second = 0
# for i in range(1):
#     for j in range(5):
#         second += math.exp(output[i][j])
# res = 0
# res = (first + math.log(second))
# print("自己的计算结果：")
# print(res)