问题六：

问题五：这里是怎么得到的？

问题四：为什么会是如下结果？

torch.bernoulli(a)怎么是这个结果？

问题1：torch各个类型数据格式如何转换？数据类型在官方文档torch.Tensor中，有八种类型。

#尝试一
i32=torch.IntTensor([1,2,3])
i64=torch.LongTensor([1,2,3])
#两种转换都报错
#new_i64=torch.IntTensor(i64)
#new_i32=torch.LongTensor(i32)
#didn't match because some of the arguments have invalid types: (!torch.LongTensor!)

#尝试二
new_i32=i32.long()
print(torch.equal(new_i32,i64))  #True
#torch.Tensor对应八种数据转换,各种数据可以相互转换
i32.float()
i32.double()
i32.half()
i32.byte()
i32.char()
i32.short()
i32.int()
i32.long()

问题2：官方文档中sequence of tensors是什么意思？在torch.stack(sequence, dim=0, out=None).

 是tensors构成的序列，可以为列表，也可以为元组。

#torch.stack(sequence, dim=0, out=None) 连接Tensors
i32=torch.Tensor([1,2,3])
print(torch.stack([i32,i32,i32]))  #默认dim=0,以列为基准
#  1  2  3
#  1  2  3
#  1  2  3
# [torch.FloatTensor of size 3x3]
print(torch.stack([i32,i32,i32],dim=1))
#  1  1  1
#  2  2  2
#  3  3  3
# [torch.FloatTensor of size 3x3]
print(torch.stack((i32,i32,i32),dim=1))
#  1  1  1
#  2  2  2
#  3  3  3
# [torch.FloatTensor of size 3x3]

问题3：为什么有如下Tensor格式区别？有的是size 3，有的是size 4×1 ？

torch.from_numpy(np.array([1,2,3]))   #torch.IntTensor of size 3
torch.from_numpy(np.array([1.0,2,3])) #torch.DoubleTensor of size 3
torch.nonzero(torch.Tensor([1,2,3,0,4]))==torch.Tensor([0,1,2,4])  #nonzero  非0元素所在位置
# TypeError: eq received an invalid combination of arguments - got (torch.FloatTensor), but expected one of:
#  * (int value)
#       didn't match because some of the arguments have invalid types: (!torch.FloatTensor!)
#  * (torch.LongTensor other)
#       didn't match because some of the arguments have invalid types: (!torch.FloatTensor!)

#注意上面代码中两者数据格式类型不一致，torch.FloatTensor   torch.LongTensor

#torch.unsqueeze(input,dim,out=None)
m=torch.Tensor([1,2,3,4])
print(m)                     #torch.FloatTensor of size 4
m_zero=torch.unsqueeze(m,0) print(m_zero) #torch.FloatTensor of size 1x4 m_one=torch.unsqueeze(m,1) print(m_one) #torch.FloatTensor of size 4x1  m_zero_to_m=torch.squeeze(m_zero) print(m_zero_to_m) #torch.FloatTensor of size 4 print(m==m_zero_to_m) #torch.ByteTensor of size 4 # 1 # 1 # 1 # 1

print(m.equal(m_zero_to_m))  True

可见为两种不同数据类型，可以通过unsqueeze和squeeze来相互转化。判断两个Tensor是否相等，用equal

问题4、