import torch

targets = torch.tensor([5, 3, 2, 1])

targets_to_one_hot = torch.nn.functional.one_hot(targets) ? # 默認(rèn)按照targets其中的最大值+1作為one_hot編碼的長度
# result:?
# tensor(
# [0, 0, 0, 0, 0, 1],
# [0, 0, 0, 1, 0, 0],
# [0, 0, 1, 0, 0, 0],
# [0, 1, 0, 0, 0, 0]
#)

targets_to_one_hot = torch.nn.functional.one_hot(targets, num_classes=7) ?3# 指定one_hot編碼長度為7
# result:?
# tensor(
# [0, 0, 0, 0, 0, 1, 0],
# [0, 0, 0, 1, 0, 0, 0],
# [0, 0, 1, 0, 0, 0, 0],
# [0, 1, 0, 0, 0, 0, 0]
#)

y = torch.empty(3, dtype=torch.long).random_(5)

y = torch.Tensor(2,3).random_(10)

y = torch.randn(3,4).random_(10)

y.type
y.dtype

tensor.long()/int()/float()
long(),int(),float() 實(shí)現(xiàn)類型的轉(zhuǎn)化

def one_hot(y):
? ? '''
? ? y: (N)的一維tensor，值為每個(gè)樣本的類別
? ? out:
? ? ? ? y_onehot: 轉(zhuǎn)換為one_hot 編碼格式
? ? '''
? ? y = y.view(-1, 1)
? ? # y_onehot = torch.FloatTensor(3, 5)
? ? # y_onehot.zero_()

? ? y_onehot = torch.zeros(3,5) ?# 等價(jià)于上面
? ? y_onehot.scatter_(1, y, 1)
? ? return y_onehot

y = torch.empty(3, dtype=torch.long).random_(5) #標(biāo)簽

res = one_hot(y) ?# 轉(zhuǎn)化為One_hot類型

# One_hot類型標(biāo)簽轉(zhuǎn)化為整數(shù)型列表的兩種方法

h = torch.argmax(res,dim=1)
_,h1 = res.max(dim=1)

a=torch.randn(1,1,3,768)

print(a.shape) #torch.Size([1, 1, 3, 768])
b=a.expand(2,-1,-1,-1)

print(b.shape) #torch.Size([2, 1, 3, 768])

c=a.expand(2,1,3,768)
print(c.shape) #torch.Size([2, 1, 3, 768])

a=torch.randn(2,1,768)
print(a)
print(a.shape) #torch.Size([2, 1, 768])
b=a.repeat(1,2,1)
print(b)
print(b.shape) #torch.Size([2, 2, 768])
c=a.repeat(3,3,3)
print(c)
print(c.shape) #torch.Size([6, 3, 2304])