教程Python代碼如下：

import torch

import torch.nn as nn

import numpy as np

"""softmax把分類(lèi)輸出標(biāo)準(zhǔn)化成概率分布，cross-entropy（交叉熵）刻畫(huà)預(yù)測(cè)分類(lèi)和真實(shí)結(jié)果之間的相似度"""

def softmax(x):

??return np.exp(x) / np.sum(np.exp(x), axis=0)

x = np.array([2.0, 1.0, 0.1])

outputs = softmax(x)

print('softmax numpy:', outputs)

loss = nn.CrossEntropyLoss()

# 3 samples

Y = torch.tensor([2, 0, 1])

# nsamples x nclasses = 3*3

Y_pred_good = torch.tensor([[0.1, 1.0, 2.1], [2.0, 1.0, 0.1], [0.1, 3.0, 0.1]])

Y_pred_bad = torch.tensor([[2.1, 1.0, 0.1], [0.1, 1.0, 2.1], [0.1, 3.0, 0.1]])

l1 = loss(Y_pred_good, Y)

l2 = loss(Y_pred_bad, Y)

print(l1.item())

print(l2.item())

_, predictions1 = torch.max(Y_pred_good, 1)

_, predictions2 = torch.max(Y_pred_bad, 1)

print(predictions1)

print(predictions2)